Systems and methods for dynamically generating optimal routes for vehicle delivery management

ABSTRACT

A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/544,559, filed on Aug. 19, 2019, entitled “SYSTEMS ANDMETHODS FOR DYNAMICALLY GENERATING OPTIMAL ROUTES FOR VEHICLE DELIVERYMANAGEMENT,” which claims the benefit of priority to U.S. ProvisionalPatent Application No. 62/806,258, filed on Feb. 15, 2019, entitled“SYSTEMS AND METHODS FOR DYNAMICALLY GENERATING OPTIMAL ROUTES FORVEHICLE DELIVERY MANAGEMENT,” and to U.S. Provisional Patent ApplicationNo. 62/843,729, filed on May 6, 2019, entitled “SYSTEMS AND METHODS FORDYNAMICALLY GENERATING OPTIMAL ROUTES FOR VEHICLE DELIVERY MANAGEMENT,”the entire contents of each of which are hereby incorporated byreference herein.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to vehicle routing, and, moreparticularly, to intelligent and dynamic routing of unautomated andautonomous vehicles for maximizing cargo delivery revenue.

BACKGROUND

It is well known to use vehicles for delivery of cargo, includingpersons and items. Newer methods of transporting such cargo are beingimplemented, such as ride-sharing and businesses contracting with thirdparties for package delivery. However, current systems require users topick and choose discrete jobs. For example, a driver for a ride-shareservice seeks individual persons (or groups thereof) to provide ridesto. Likewise, third-party delivery contractors accept individualdelivery jobs. Current systems do not provide users with any strategiesfor maximizing revenue. Accordingly, it is left up to users to attemptto maximize their revenue, time, and/or other resources as they chooseindividual jobs or tasks. Moreover, many known systems may limit orplace restrictions on concurrent jobs, such that users acceptconsecutive jobs and may be deprived of the advantages (in time andrevenue) of concurrent jobs.

BRIEF SUMMARY

The present embodiments may relate to, inter alia, systems and methodsfor optimizing delivery routing for a plurality of vehicles. Vehicleusers (e.g., owners, lessors, fleet managers, etc.) may register with avehicle routing system to receive vehicle routing services therefrom.The vehicle users may submit “definitions” of their vehicle thatdescribe how they want their vehicle to be used (e.g., the times andlocations that the vehicle is available to make deliveries). The vehiclerouting system may receive a plurality of tasks (e.g., delivering cargoincluding person(s) and/or object(s)) to be completed, and may analyzethe vehicle definitions and the plurality of tasks to generate anoptimal route for each vehicle that maximizes the vehicle's profitgeneration. In the exemplary embodiment, the vehicle routing system mayinclude artificial intelligence and/or deep learning functionality togenerate the optimal routes.

In addition, the vehicle routing system may collect and process sensordata from the plurality of vehicles, as the vehicle operate accordingthe optimal routes (e.g., performing delivery tasks). The sensor datamay characterize use/performance of the vehicle, an ambient environmentaround the vehicle (e.g., weather, traffic), local infrastructure, andmore. The vehicle routing system may process the sensor data (e.g.,using the artificial intelligence and/or deep learning functionality) togenerate vehicle analytics for each vehicle, identify trends, updateoptimal routes, and/or make recommendations.

In the exemplary embodiment, the vehicle routing system maintains amanagement hub software application (“app”) that enables users to viewvehicle analytics and recommendations made by the vehicle routingsystem. The management hub may additionally enable users to update theiruser preferences or vehicle definitions of registered vehicles.Additionally or alternatively, the management hub app provides usersaccess to a plurality of other services provided by the vehicle routingsystem, including maintenance/repair services, financial services,marketing services, and the like.

In one aspect, a vehicle routing and analytics (VRA) computing devicefor generating an optimal route for a vehicle to travel that maximizespotential revenue for operation of the vehicle and generating analyticsassociated with operation of the vehicle is provided. The VRA computingdevice is communicatively coupled to the vehicle, the vehicle having aplurality of sensors disposed thereon and configured to collect sensordata during operation thereof, and the VRA computing device includes atleast one processor in communication with a memory. The at least oneprocessor is programmed to receive, from a vehicle user associated withthe vehicle, a vehicle definition for the vehicle, the vehicledefinition including availability parameters and delivery preferencesassociated with the vehicle, and generate, based in part on the vehicledefinition, an optimal route for the vehicle that includes a scheduledlist of a plurality of tasks for the vehicle to perform, each taskhaving an associated respective cargo to be delivered, pick-up time,delivery time, pick-up location, delivery location, and task value forcompletion of the respective task. The optimal route maximizes thepotential revenue for operation of the vehicle and completion of theplurality of tasks within a period of time associated with the optimalroute. The processor is also programmed to transmit the optimal route tothe vehicle for operation of the vehicle according to the optimal route,and receive, from the vehicle, sensor data during operation of thevehicle according to the optimal route. The sensor data is generated atsensors disposed in at least one of: (i) the vehicle, and (ii) a usercomputing device of the vehicle user. The processor is furtherprogrammed to process the received sensor data to generate vehicleanalytics associated with a performance of the vehicle, the vehicleanalytics including a level of adherence to the optimal route andrevenue statistics for a completed portion of the optimal route and foran uncompleted portion of the optimal route, generate one or more visualrepresentations of the vehicle analytics and instructions for display ofthe one or more visual representations of the vehicle analytic, andtransmit the instructions to the user computing device of the vehicleuser, the instructions causing execution of a management hub applicationon the user computing device to display the one or more visualrepresentations with a user interface of the executed management hubapplication. The VRA computing device may include additional, less,and/or alternative functionality, including that described herein.

In another aspect, a computer-implemented method for generating anoptimal route for a vehicle to travel that maximizes potential revenuefor operation of the vehicle and generating analytics associated withoperation of the vehicle is provided. The method is implemented by avehicle routing and analytics (VRA) computing device including at leastone processor and communicatively coupled to the vehicle, the vehiclehaving a plurality of sensors disposed thereon and configured to collectsensor data during operation thereof. The method includes receiving,from a vehicle user associated with the vehicle, a vehicle definitionfor the vehicle, the vehicle definition including availabilityparameters and delivery preferences associated with the vehicle, andgenerating, based in part on the vehicle definition, an optimal routefor the vehicle that includes a scheduled list of a plurality of tasksfor the vehicle to perform, each task having an associated respectivecargo to be delivered, pick-up time, delivery time, pick-up location,delivery location, and task value for completion of the respective task.The optimal route maximizes the potential revenue for operation of thevehicle and completion of the plurality of tasks within a period of timeassociated with the optimal route. The method also includes transmittingthe optimal route to the vehicle for operation of the vehicle accordingto the optimal route, and receiving, from the vehicle, sensor dataduring operation of the vehicle according to the optimal route. Thesensor data is generated at sensors disposed in at least one of: (i) thevehicle, and (ii) a user computing device of the vehicle user. Themethod further includes processing the received sensor data to generatevehicle analytics associated with a performance of the vehicle, thevehicle analytics including a level of adherence to the optimal routeand revenue statistics for a completed portion of the optimal route andfor an uncompleted portion of the optimal route, generating one or morevisual representations of the vehicle analytics and instructions fordisplay of the one or more visual representations of the vehicleanalytics, and transmitting the instructions to the user computingdevice of the vehicle user, the instructions causing execution of amanagement hub application on the user computing device to display theone or more visual representations within a user interface of theexecuted management hub application. The method may include additional,fewer, and/or alternative steps, including those described herein.

In a further aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonfor generating an optimal route for a vehicle to travel that maximizespotential revenue for operation of the vehicle and generating analyticsassociated with operation of the vehicle is provided. When executed by aprocessor of a vehicle routing and analytics (VRA) computing devicecommunicatively coupled to the vehicle, the vehicle having a pluralityof sensors disposed thereon and configured to collect sensor data duringoperation thereof, the computer-executable instructions cause theprocessor to receive, from a vehicle user associated with the vehicle, avehicle definition for the vehicle, the vehicle definition includingavailability parameters and delivery preferences associated with thevehicle, and generate, based in part on the vehicle definition, anoptimal route for the vehicle that includes a scheduled list of aplurality of tasks for the vehicle to perform, each task having anassociated respective cargo to be delivered, pick-up time, deliverytime, pick-up location, delivery location, and task value for completionof the respective task. The optimal route maximizes the potentialrevenue for operation of the vehicle and completion of the plurality oftasks within a period of time associated with the optimal route. Thecomputer-executable instructions may also cause the at least oneprocessor to transmit the optimal route to the vehicle for operation ofthe vehicle according to the optimal route, and receive, from thevehicle, sensor data during operation of the vehicle according to theoptimal route. The sensor data is generated at sensors disposed in atleast one of: (i) the vehicle, and (ii) a user computing device of thevehicle user. The computer-executable instructions may also cause the atleast one processor to process the received sensor data to generatevehicle analytics associated with a performance of the vehicle, thevehicle analytics including a level of adherence to the optimal routeand revenue statistics for a completed portion of the optimal route andfor an uncompleted portion of the optimal route, generate one or morevisual representations of the vehicle analytics and instructions fordisplay of the one or more visual representations of the vehicleanalytics, and transmit instructions to the user computing device of thevehicle user, the instructions causing execution of a management hubapplication on the user computing device to display the one or morevisual representations within a user interface of the executedmanagement hub application. The computer-executable instructions maycause additional, less, and/or alternative functionality, including thatdescribed herein.

In one aspect, a vehicle routing and analytics (VRA) computing devicefor generating an optimal route for a vehicle that maximizes potentialrevenue for operation of the vehicle may be provided. The VRA computingdevice may include at least one processor in communication with amemory, wherein the at least one processor may be programmed to retrievea vehicle definition for the vehicle, the vehicle definition includingavailability parameters and delivery preferences associated with thevehicle. The at least one processor may also be programmed to retrieve,based in part on the vehicle definition, a plurality of task definitionsdefining a respective plurality of tasks, each task including arespective cargo to be delivered, pick-up time, delivery time, pick-uplocation, delivery location, and task value. The at least one processormay be further programmed to generate, by executing at least one ofartificial intelligence and deep learning functionality using thevehicle definition and the plurality of task definitions, an optimalroute for the vehicle that includes a scheduled list of a subset of theplurality of tasks for the vehicle to perform, wherein the optimal routemaximizes the potential revenue for operation of the vehicle within aperiod of time associated with the optimal route, and to transmit theoptimal route to the vehicle for operation of the vehicle according tothe optimal route. The VRA computing device may include additional,less, and/or alternative functionality, including that described herein.

In another aspect, a computer-implemented method for generating anoptimal route for a vehicle to travel that maximizes potential revenuefor operation of the vehicle may be provided. The method may beimplemented by a vehicle routing and analytics (VRA) computing deviceincluding at least one processor. The method may include retrieving avehicle definition for the vehicle, the vehicle definition includingavailability parameters and delivery preferences associated with thevehicle. The method may also include retrieving, based in part on thevehicle definition, a plurality of task definitions defining arespective plurality of tasks, each task including a respective cargo tobe delivered, pick-up time, delivery time, pick-up location, deliverylocation, and task value. The method may further include generating, byexecuting at least one of artificial intelligence and deep learningfunctionality using the vehicle definition and the plurality of taskdefinitions, an optimal route for the vehicle that includes a scheduledlist of a subset of the plurality of tasks for the vehicle to perform,wherein the optimal route maximizes the potential revenue for operationof the vehicle within a period of time associated with the optimalroute, and transmitting the optimal route to the vehicle for operationof the vehicle according to the optimal route. The method may includeadditional, fewer, and/or alternative steps, including those describedherein.

In a further aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonfor generating an optimal route for a vehicle to travel that maximizespotential revenue for operation of the vehicle may be provided. Whenexecuted at least one processor of a vehicle routing and analytics (VRA)computing device, the computer-executable instructions may cause the atleast one processor to retrieve a vehicle definition for the vehicle,the vehicle definition including availability parameters and deliverypreferences associated with the vehicle. The computer-executableinstructions may also cause the at least one processor to retrieve,based in part on the vehicle definition, a plurality of task definitionsdefining a respective plurality of tasks, each task including arespective cargo to be delivered, pick-up time, delivery time, pick-uplocation, delivery location, and task value. The computer-executableinstructions may further cause the at least one processor to generate,by executing at least one of artificial intelligence and deep learningfunctionality using the vehicle definition and the plurality of taskdefinitions, an optimal route for the vehicle that includes a scheduledlist of a subset of the plurality of tasks for the vehicle to perform,wherein the optimal route maximizes the potential revenue for operationof the vehicle within a period of time associated with the optimalroute, and transmit the optimal route to the vehicle for operation ofthe vehicle according to the optimal route. The computer-executableinstructions may cause additional, less, and/or alternativefunctionality, including that described herein.

In yet another aspect, a vehicle routing and analytics (VRA) computingdevice for generating an optimal route for a vehicle to travel thatmaximizes potential revenue for operation of the vehicle may beprovided. The VRA computing device may be communicatively coupled to aplurality of vehicles, each vehicle having a plurality of sensorsdisposed thereon and configured to collect sensor data during operationof the respective vehicle. The VRA computing device may include at leastone processor in communication with a memory, wherein the at least oneprocessor may be programmed to retrieve a vehicle definition for a firstvehicle of the plurality of vehicles, the vehicle definition includingavailability parameters and delivery preferences associated with thefirst vehicle. The at least one processor may also be programmed toretrieve, based in part on the vehicle definition, a plurality of taskdefinitions defining a respective plurality of tasks, each taskincluding a respective cargo to be delivered, pick-up time, deliverytime, pick-up location, delivery location, and task value. The at leastone processor may be further programmed to generate, by executing atleast one of artificial intelligence and deep learning functionalityusing the vehicle definition and the plurality of task definitions, anoptimal route for the vehicle that includes a scheduled list of a subsetof the plurality of tasks for the vehicle to perform, wherein theoptimal route maximizes the potential revenue for operation of thevehicle within a period of time associated with the optimal route. Theat least one processor may still further be programmed to transmit theoptimal route to the first vehicle for operation of the vehicleaccording to the optimal route, and receive, from the first vehicle,sensor data during operation of the first vehicle according to theoptimal route. The VRA computing device may include additional, less,and/or alternative functionality, including that described herein.

In another aspect, a computer-implemented method for generating anoptimal route for a vehicle to travel that maximizes potential revenuefor operation of the vehicle may be provided. The method may beimplemented by a vehicle routing and analytics (VRA) computing deviceincluding at least one processor and a memory, the VRA computing devicecommunicatively coupled to a plurality of vehicles, each vehicle havinga plurality of sensors disposed thereon and configured to collect sensordata during operation of the respective vehicle. The method may includeretrieving a vehicle definition for a first vehicle of the plurality ofvehicles, the vehicle definition including availability parameters anddelivery preferences associated with the first vehicle. The method mayalso include retrieving, based in part on the vehicle definition, aplurality of task definitions defining a respective plurality of tasks,each task including a respective cargo to be delivered, pick-up time,delivery time, pick-up location, delivery location, and task value. Themethod may further include generating, by executing at least one ofartificial intelligence and deep learning functionality using thevehicle definition and the plurality of task definitions, an optimalroute for the vehicle that includes a scheduled list of a subset of theplurality of tasks for the vehicle to perform, wherein the optimal routemaximizes the potential revenue for operation of the vehicle within aperiod of time associated with the optimal route. The method may stillfurther include transmitting the optimal route to the first vehicle foroperation of the vehicle according to the optimal route, and receiving,from the first vehicle, sensor data during operation of the firstvehicle according to the optimal route. The method may includeadditional, fewer, and/or alternative steps, including those describedherein.

In a further aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonfor generating an optimal route for a vehicle to travel that maximizespotential revenue for operation of the vehicle may be provided. Whenexecuted by at least one processor of a vehicle routing and analytics(VRA) computing device communicatively coupled to a plurality ofvehicles, each vehicle having a plurality of sensors disposed thereonand configured to collect sensor data during operation of the respectivevehicle, the computer-executable instructions may cause the at least oneprocessor to retrieve a vehicle definition for a first vehicle of theplurality of vehicles, the vehicle definition including availabilityparameters and delivery preferences associated with the first vehicle.The computer-executable instructions may also cause the at least oneprocessor to retrieve, based in part on the vehicle definition, aplurality of task definitions defining a respective plurality of tasks,each task including a respective cargo to be delivered, pick-up time,delivery time, pick-up location, delivery location, and task value. Thecomputer-executable instructions may further cause the at least oneprocessor to generate, by executing at least one of artificialintelligence and deep learning functionality using the vehicledefinition and the plurality of task definitions, an optimal route forthe vehicle that includes a scheduled list of a subset of the pluralityof tasks for the vehicle to perform, wherein the optimal route maximizesthe potential revenue for operation of the vehicle within a period oftime associated with the optimal route. The computer-executableinstructions may also cause the at least one processor to transmit theoptimal route to the first vehicle for operation of the vehicleaccording to the optimal route, and receive, from the first vehicle,sensor data during operation of the first vehicle according to theoptimal route. The computer-executable instructions may causeadditional, less, and/or alternative functionality, including thatdescribed herein.

In another aspect, a computer-implemented method of directing anautonomously floating delivery autonomous vehicle may be provided. Themethod may include receiving, via one or more processors, servers,and/or transceivers, autonomous vehicle (AV) condition data frommultiple autonomous vehicles and other sources (such as smartinfrastructure or intelligent homes) via wireless communication and/ordata transmission over one or more radio frequency links, the conditiondata being generated by autonomous vehicle-mounted sensors andindicating weather, road, traffic, congestion, and/or accidentconditions. The method may also include retrieving from a memory unit orreceiving via wireless communication or data transmission over one ormore radio links, via one or more processors, servers, and/ortransceivers, multiple service requests generated by multiple customercomputing devices. Each service request may include a pick-up anddrop-off address, location, or coordinates, and information identifyingone or more passengers or type and weight of one or more packages. Themethod may further include calculating, via one or more processors orservers, an overall route that the floating delivery autonomous vehiclewill travel from an origination point to a final destination. Theoverall route may include each pick-up and drop-off point of thepassengers and packages (as identified in the service requests) as alowest cost waypoint along or within the overall route, the waypointsbeing calculated as being lowest cost based upon AV condition dataand/or passenger or package weight or size information. The method maystill further include routing or directing, via one or more processorsor servers, the delivery autonomous vehicle along the overall route topick-up and drop-off passengers and packages at each intermediatewaypoint along the overall route, receiving, via one or more processorsor servers, a new or additional electronic service request for pick-upand delivery of an additional passenger and/or package, the additionalelectronic service request including pick-up and drop-off point orlocation information, and continuing, via one or more processors,servers, and/or transceivers, to receive updated AV condition data viawireless communication or data transmission over one or more radiolinks, the condition data including traffic, weather, congestion, road,and/or accident data. The condition data may be generated by autonomousvehicles or other sources, such as smart infrastructure, intelligenthomes, or mobile devices. The method may include, while the delivery AVis floating along the overall route, dynamically updating the overallroute, via one or more processors and/or servers, by calculating thelowest cost route that includes the intermediate pick-up and drop-offlocations or points in the additional service request(s) as additionalwaypoints along the overall route. The lowest cost update route may becalculated based upon the update AV condition data or other datareceived since the overall route was initially calculated. The methodmay also include directing, via one or more processors and/or servers,the delivery AV along the dynamically updated overall route, andupdating, via one or more processors and/or servers, a status hub oruser interface displaying a current status and/or location of eachpassenger and/or package. The method may include additional, fewer,and/or alternative steps, including those described herein.

In a further aspect, a computer system for remotely and/or locallydirecting an autonomously floating delivery autonomous vehicle may beprovided. The computer system may include one or more local or remoteprocessors, sensors, transceivers, and/or servers configured to receiveautonomous vehicle (AV) condition data from multiple autonomous vehiclesand other sources (such as smart infrastructure or intelligent homes)via wireless communication and/or data transmission over one or moreradio frequency links. The condition data may be generated by autonomousvehicle-mounted sensors and indicating weather, road, traffic,congestion, or accident conditions. The computer system may beconfigured to retrieve from a memory unit or receive via wirelesscommunication or data transmission over one or more radio links multipleservice requests generated by multiple customer computing devices, eachservice request including a pick-up and drop-off address, location, orcoordinates, each service request including information identifying oneor more passengers or type and weight of one or more packages. Thecomputer system may also be configured to calculate an overall routethat the floating delivery autonomous vehicle will travel from anorigination point to a final destination, the overall route includingeach pick-up and drop-off point of the passengers and packages (asidentified in the service requests) as a lowest cost waypoint along orwithin the overall route. The waypoints may be calculated as beinglowest cost based upon AV condition data and/or passenger or packageweight or size information. The computer system may be furtherconfigured to route or direct the delivery autonomous vehicle along theoverall route to pick-up and drop-off passengers and packages at eachintermediate waypoint along the overall route, receive a new oradditional electronic service request for pick-up and delivery of anadditional passenger and/or package, the additional electronic servicerequest including pick-up and drop-off point or location information,and continue to receive updated AV condition data via wirelesscommunication or data transmission over one or more radio links, thecondition data including traffic, weather, congestion, road, and/oraccident data. The condition data may be generated by autonomousvehicles or other sources, such as smart infrastructure, intelligenthomes, or mobile devices. In addition, the computer system may beconfigured to, while the delivery AV is floating along the overallroute, dynamically update the overall route by calculating the lowestcost route that includes the intermediate pick-up and drop-off locationsor points in the additional service request(s) as additional waypointsalong the overall route. The lowest cost update route may be calculatedbased upon the update AV condition data or other data received since theoverall route was initially calculated. The computer system may be stillfurther configured to direct the delivery AV along the dynamicallyupdated overall route; and update a status hub or user interfacedisplaying a current status and/or location of each passenger and/orpackage. The computer system may have additional, less, and/oralternative functionality, including functionality as described herein.

In another aspect, at least one non-transitory computer-readable storagemedium having computer-executable instructions thereon may be provided,wherein, when executed by at least one processor of a computer systemfor remotely and/or locally directing an autonomously floating deliveryautonomous vehicle, the computer system including one or more local orremote processors, sensors, transceivers, and/or servers, thecomputer-executable instructions cause the at least one processor toreceive autonomous vehicle (AV) condition data from multiple autonomousvehicles and other sources (such as smart infrastructure or intelligenthomes) via wireless communication and/or data transmission over one ormore radio frequency links. The condition data may be generated byautonomous vehicle-mounted sensors and indicating weather, road,traffic, congestion, or accident conditions. The computer-executableinstructions may cause the at least one processor to retrieve from amemory unit or receive via wireless communication or data transmissionover one or more radio links multiple service requests generated bymultiple customer computing devices, each service request including apick-up and drop-off address, location, or coordinates, each servicerequest including information identifying one or more passengers or typeand weight of one or more packages. The computer-executable instructionsmay also cause the at least one processor to calculate an overall routethat the floating delivery autonomous vehicle will travel from anorigination point to a final destination. The overall route may includeeach pick-up and drop-off point of the passengers and packages (asidentified in the service requests) as a lowest cost waypoint along orwithin the overall route, the waypoints being calculated as being lowestcost based upon AV condition data and/or passenger or package weight orsize information. The computer-executable instructions may further causethe at least one processor to route or direct the delivery autonomousvehicle along the overall route to pick-up and drop-off passengers andpackages at each intermediate waypoint along the overall route, receivea new or additional electronic service request for pick-up and deliveryof an additional passenger and/or package, the additional electronicservice request including pick-up and drop-off point or locationinformation, and continue to receive updated AV condition data viawireless communication or data transmission over one or more radiolinks, the condition data including traffic, weather, congestion, road,and/or accident data, the condition data may be generated by autonomousvehicles or other sources, such as smart infrastructure, intelligenthomes, or mobile devices. The computer-executable instructions may stillfurther cause the at least one processor to, while the delivery AV isfloating along the overall route, dynamically update the overall routeby calculating the lowest cost route that includes the intermediatepick-up and drop-off locations or points in the additional servicerequest(s) as additional waypoints along the overall route. The lowestcost update route may be calculated based upon the update AV conditiondata or other data received since the overall route was initiallycalculated. The computer-executable instructions may also cause the atleast one processor to direct the delivery AV along the dynamicallyupdated overall route, and update a status hub or user interfacedisplaying a current status and/or location of each passenger and/orpackage. The computer-executable instructions may cause additional,less, and/or alternative functionality, including that described herein.

In a still further aspect, a computer system for directing a floatingdelivery autonomous vehicle may be provided. The computer system mayinclude at least one of: one or more processors, one or more sensors,one or more transceivers, and one or more servers. The computer systemmay be configured to receive autonomous vehicle (AV) condition data fromat least one source including a plurality of AVs, the AV condition datagenerated by AV-mounted sensors and indicating weather, road, traffic,congestion, and/or accident conditions, and retrieve, from a memoryunit, a plurality of service requests generated by a correspondingplurality of customer computing devices, each service request includinga pick-up location, a drop-off location, and information identifying (i)one or more passengers, and/or (ii) a type and a weight of one or morepackages. The computer system may also be configured to calculate anoverall route that the floating delivery AV will travel from anorigination location to a final location, the overall route including arespective pick-up and drop-off location identified in a subset of theplurality of service requests, including calculating the overall routeas a lowest cost route between all of the respective pick-up anddrop-off locations as waypoints, based upon at least one of: (i) the AVcondition data, and/or (ii) passenger or package information identifiedin the subset of service requests. The computer system may be configuredto direct the delivery AV to travel the overall route to each pick-upand drop-off location and to pick-up and drop off the (i) one or morepassengers and/or (ii) one or more packages at each respective pick-upand drop-off location as indicated in the subset of the plurality ofservice requests. The computer system may be still further configured toreceive an additional service request for pick-up and drop-off of anadditional (i) one or more passengers and/or (ii) one or more packages,the additional service request including a corresponding pick-up anddrop-off locations, continuously receive updated AV condition data, and,while directing the delivery AV along the overall route, dynamicallyupdate the overall route by calculating an updated lowest cost routethat includes the pick-up and drop-off locations in the additionalservice request as additional waypoints along the overall route, basedupon at least one of (i) the updated AV condition data and/or (ii) otherdata received since the overall route was initially calculated. Thecomputer system may be configured to direct the delivery AV along thedynamically updated overall route, and update a remote status hub withlocation information associated with the delivery AV, the status hubconfigured to display at least one of a current status and currentlocation of each (i) one or more passengers or (ii) one or more packagestransported by the delivery AV. The computer system may have additional,less, and/or alternative functionality, including functionality asdescribed herein.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects. Inaddition, although certain steps of the exemplary processes arenumbered, having such numbering does not indicate or imply that thesteps necessarily have to be performed in the order listed. The stepsmay be performed in the order indicated or in another order.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the systems andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed systemsand methods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and are instrumentalitiesshown, wherein:

FIG. 1 illustrates a schematic diagram of an exemplary vehicle;

FIG. 2 illustrates a schematic diagram of an exemplary vehicle routingsystem;

FIG. 3 illustrates a schematic diagram of an exemplary vehicle routingand analytics (VRA) computing device that may be used in the vehiclerouting system shown in FIG. 2;

FIG. 4A illustrates a conventional delivery schedule;

FIG. 4B illustrates an exemplary optimal route that may be generated bythe VRA computing device shown in FIG. 2 and implemented by the vehicleshown in FIG. 1;

FIG. 5 illustrates an exemplary task that may be subject to routecontinuation and divided between optimal routes of vehicles shown inFIG. 1;

FIGS. 6 and 7 depict exemplary screen captures of a management hubsoftware application (“app”) maintained using the vehicle routing systemshown in FIG. 2;

FIG. 8 illustrates a schematic diagram of an exemplary user computingdevice that may be used in the vehicle routing system shown in FIG. 2;

FIG. 9 illustrates a flow chart of an exemplary computer-implementedmethod for generating an optimal route for a vehicle that maximizespotential revenue for operation of the vehicle, using the vehiclerouting system shown in FIG. 2;

FIG. 10 illustrates a flow chart of another exemplarycomputer-implemented method for generating an optimal route for avehicle that maximizes potential revenue for operation of the vehicleand generating analytics associated with operation of the vehicle, usingthe vehicle routing system shown in FIG. 2;

FIG. 11 illustrates a flow chart of yet another exemplarycomputer-implemented method for generating an optimal route for avehicle that maximizes potential revenue for operation of the vehicleand generating analytics associated with operation of the vehicle, usingthe vehicle routing system shown in FIG. 2;

FIG. 12A depicts an exemplary FLOAT computing environment thatfacilitates autonomous vehicle autonomously floating from oneopportunity to another; and

FIG. 12B depicts an exemplary FLOAT computer-implemented methodfacilitates autonomous vehicle autonomously floating from oneopportunity to another.

The Figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

The present embodiments may relate to, inter alia, systems and methodsfor optimizing delivery routing for a plurality of vehicles. Vehicleusers (e.g., owners, lessors, fleet managers, etc.) may register with avehicle routing system to receive vehicle routing services therefrom.The vehicle users may submit “definitions” of their vehicle thatdescribe how they want their vehicle to be used (e.g., the times andlocations that the vehicle is available to make deliveries). The vehiclerouting system may receive a plurality of tasks (e.g., delivering cargoincluding person(s) and/or object(s)) to be completed, and may analyzethe vehicle definitions and the plurality of tasks to generate anoptimal route for each vehicle that maximizes the vehicle's profitgeneration. In the exemplary embodiment, the vehicle routing system mayinclude artificial intelligence and/or machine or deep learningfunctionality to generate the optimal routes.

In addition, the vehicle routing system may collect and process sensordata from the plurality of vehicles, as the vehicles operate accordingthe optimal routes (e.g., performing delivery tasks). The sensor datamay characterize use/performance of the vehicle, an ambient environmentaround the vehicle (e.g., weather, traffic), local infrastructure, andmore. The vehicle routing system may process the sensor data (e.g.,using the artificial intelligence and/or machine or deep learningfunctionality) to generate vehicle analytics for each vehicle, identifytrends, update optimal routes, and/or make recommendations.

In the exemplary embodiment, the vehicle routing system maintains amanagement hub app that enables users to view vehicle analytics andrecommendations made by the vehicle routing system. The management hubmay additionally enable users to update their user preferences orvehicle definitions of registered vehicles. Additionally oralternatively, the management hub app provides users access to aplurality of other services provided by the vehicle routing system,including maintenance/repair services, financial services, marketingservices, and the like.

“Vehicle,” as used herein, may refer generally to any vehicle owned,operated, and/or used by one or more vehicle users. A vehicle mayinclude any kind of vehicle, such as, for example, cars, trucks,all-terrain vehicles (ATVs), motorcycles, recreational vehicles (RVs),snowmobiles, boats, autonomous vehicles, semi-autonomous vehicles,user-driven or user-operated vehicles, industrial vehicles (e.g.,construction vehicles), “riding” lawnmowers, farm equipment, planes,helicopters, bicycles, flying cars, robo-taxis, self-driving taxis,and/or any kind of land-, water-, or air-based vehicle.

“Vehicle user,” as used herein, may refer generally to a person who isresponsible for the vehicle, and who has access to use of the vehicle.Vehicle users may include owners, lessors, and/or renters, for example,of a vehicle. A vehicle user may be responsible for or have access to aplurality of vehicles, referred to herein as a “fleet.” Vehicle usersmay be personal vehicle users (e.g., may be responsible for and haveaccess to one or more vehicles for personal use) and/or may be corporatevehicle users (e.g., corporate managers who may be responsible for andhave access to one or more vehicles associated with corporate use and/orwith a corporate entity).

“Autonomous vehicle,” as used herein, may refer generally to any vehiclethat has at least one automation system that is related to the pilotingof the vehicle (e.g., warning systems assisting in a piloting task,intervention systems performing a piloting task, control systemsperforming a piloting task). The term “unautomated vehicle” refers tovehicles in which no automation systems are present (e.g., the vehicleis being piloted by the full-time performance of a human driver, andwithout enhancements from warning or intervention systems). The terms“semi-autonomous vehicle” and “autonomous vehicle” may be usedinterchangeably in some instances, and the term “autonomous vehicle” maybe used to refer to both semi-autonomous vehicles and autonomousvehicles for purposes of convenience.

Automation systems include, for example, rear-view sensors and alarms(e.g., to detect obstacles while in reverse), anti-lock braking systems(e.g., to prevent wheel locking during deceleration), traction controlsystems (e.g., actuating brakes or reducing throttle to restore tractionif wheels begin to spin), electronic stability control and accelerationslip regulation (e.g., to prevent the car from understeering oroversteering), dynamic steering response (e.g., to correct the rate ofpower steering based upon road conditions), cruise control (e.g., tomaintain vehicle speed), autonomous cruise control (e.g., to adjustcruising speed to maintain safe distance from vehicles ahead), lane-keepassist systems (e.g., to alert the driver or adjust steering to keep thevehicle in its current lane), driver monitoring systems (e.g., to warndrivers when they become drowsy or fall asleep), adaptive headlamps(e.g., to alter the brightness or angle of headlamps), collisionavoidance systems (e.g., to warn the driver an impending collision oradjust steering to avoid impending collision), parking assistancesystems, blind spot monitoring systems, traffic sign recognitionsystems, dead man's switch systems, computer vision systems, locationdetermination systems (e.g., GPS), and navigation systems (e.g., tonavigate or assist in navigating the vehicle to a destination).

“Job” or “task,” as used interchangeably herein, may refer generally toone complete use of the vehicle, from a starting point to an endingpoint. In some cases, a task may commence when the vehicle is startedand may terminate when the vehicle is turned off. If a task is definedin this way, the vehicle may automatically track and record tasks, ascommencement and termination are simply defined. In some cases, a taskmay commence when an initial location is reached and may terminate whena destination location is reached. In such cases, the vehicle mayautomatically track and record tasks based upon, for example, locationdetermination and/or navigation systems native to the vehicle and/oractivated on a user computing device within the vehicle. In other cases,the task may be “manually” defined, such that a user designates acommencement and termination of a task (e.g., when the vehicle may beturned on and off more than once in a single use of the vehicle, or whenmultiple tasks are completed without turning off the vehicle). In suchcases, the vehicle may prompt the user to designate the commencement andtermination (e.g., using a user interface of the vehicle and/or using anapp available on a user computing device) of the task such that thevehicle may track and record the task.

As used herein, “vehicle definition” may refer generally to a record ofcharacteristics of each vehicle. The vehicle routing system describedherein may use the vehicle definition to identify available tasks that avehicle is able to complete, when generating the optimal route for asingle vehicle or a plurality of vehicles. A vehicle definition may begenerated and/or populated based upon input from a vehicle userassociated with a vehicle (e.g., an owner, lessor, corporate manager,etc.) when the vehicle user registers the vehicle with the vehiclerouting system (e.g., to receive the routing services therefrom). Eachvehicle definition includes a plurality of data elements such as one ormore of: an identifier of the vehicle (e.g., a vehicle identificationnumber (VIN), or an identifier specific to the vehicle routing system);time(s) the vehicle is available to complete tasks; geographiclocation(s) in which the vehicle is available to complete tasks; whetherthe vehicle is autonomous, semi-autonomous, or manually driven;available features of the vehicle (e.g., luggage racks, tinted windows,child locks, running boards, accessibility features, etc.); vehiclemake; vehicle model; vehicle manufacturing year or vehicle age; vehiclemileage; vehicle capacity (e.g., for cargo including persons and/orobjects); vehicle class (e.g., sedan, pick-up truck, semi-truck, plane,bicycle, etc.); and/or communication standards for the vehicle (e.g.,how the vehicle can receive commands, messages, etc.). The vehicledefinition may further include an identifier of and/or contactinformation for one or more vehicle users, such as the vehicle user thatregistered the vehicle and/or additional vehicle user(s) responsible forand/or having access to the vehicle.

Vehicle definitions may additionally or alternatively include data thatis continually or periodically updated—that is, vehicle definitions maynot be completely static but may change over time. For example, vehicledefinitions may further include data elements such as a current vehiclelocation; current vehicle capacity and/or current cargo in vehicle; arisk or performance rating of the vehicle; historical claim dataassociated with the vehicle and/or vehicle user; and/or user preferences(e.g., only accept cargo including persons, only accept cargo includingobjects, accept cargo including persons and/or objects, available forroute continuation tasks, accept or do not accept high-risk cargo,etc.).

“Task definitions,” as used herein, may refer to a record ofcharacteristics of an available or a completed task. The vehicle routingsystem described herein may use the task definition of each task, whengenerating the optimal route for a single vehicle or a plurality ofvehicles. A task definition may be generated and/or populated when atask is registered with the vehicle routing system, as described furtherherein. Each task definition includes a plurality of data elements suchas one or more of: a pick-up time, a delivery time, precisiondesignation for the pick-up time and/or the delivery time (e.g., lowprecision such as +/− thirty minutes, high precision such as +/− fiveminutes), pick-up, transport, and/or delivery restrictions (e.g., humansignature or other authorization measure required, specific type ofvehicle required or prohibited, concurrent cargo prohibited, etc.),pick-up location, delivery location, task value (e.g., a dollar amountgranted upon completion of the task); cargo type (e.g., person and/orobject); dimensions, size, and/or weight of cargo; number of cargo(e.g., two package or three persons); cargo status (e.g., high-value,high-risk, social good); cargo priority (e.g., low priority, VIP, etc.);cargo risk and/or performance rating; and/or additional requirementsand/or preferences (e.g., no route continuation, bonus if deliveredwithin a priority period of time, restrictions on transporting certaincargo or passengers concurrently, etc.). Task definitions may furtherinclude an identifier of and/or contact information for a userassociated therewith, such as a user that registered the task with thevehicle routing system (which may include an individual person, acorporate entity, etc.).

Task definitions may additionally or alternatively include data that iscontinually or periodically updated—that is, task definitions may not becompletely static but may change over time. For example, taskdefinitions may further include data elements such as a current cargolocation; and/or current task value (e.g., a bonus offered or a paymentreduced based upon poor service, etc.). It is contemplated that tasksmay have default values associated therewith for common tasks (e.g., apick up from and delivery to common or frequent locations), tasks mayhave values set on a per-task basis by whomever registers the task,tasks may have varying values based upon how successfully or timely theyare completed, and/or task values may be divided (e.g., if the cargo issubject to route continuation).

“App,” as used herein, may refer generally to a software applicationinstalled and downloaded on a user interface of the vehicle and/or auser computing device associated with a vehicle user. An app associatedwith the vehicle routing system, as described herein, may be understoodto be maintained by the vehicle routing system and/or one or morecomponents thereof. Accordingly, a “maintaining party” of the app may beunderstood to be responsible for any functionality of the app and may beconsidered to instruct other parties/components to perform suchfunctions via the app.

Exemplary Embodiments

In the example embodiment, the vehicle routing system is configured tocapture data from a plurality of sources and automatically synthesizethat data to create efficient routing strategies for one or morevehicles. For example, the vehicle routing system may receive, retrieve,capture, and/or otherwise access sensor data from the vehicles (whichmay include and/or be processed to generate usage data for the vehicle),tasks data, user data (e.g., of vehicle users registered with thevehicle routing system), and/or ambient data (e.g., weather data,traffic data, news, market data). The vehicle routing system may includeany suitable data storage capabilities, such as cloud storage, to accessand/or store any of the above data. In that way, the vehicle routingsystem may access historical and/or current (e.g., real-time or nearreal-time) data. In the exemplary embodiment, the vehicle routing systemincludes at least one vehicle routing and analytics (VRA) computingdevice. The VRA computing device is configured to perform the functionsthat may be more generally described herein as being performed by and/orattributed to the vehicle routing system.

In particular, in the exemplary embodiment, the VRA computing deviceprocesses vehicle definitions and task definitions of available tasks togenerate an optimal route for one or more vehicles. The VRA computingdevice may receive each vehicle definition, for example, from a vehicleuser associated with each vehicle when the vehicle user registers withthe vehicle routing system. For example, the vehicle user adds one ormore vehicles to an account to access the vehicle routing strategies ofthe vehicle routing system. The VRA computing device may store thevehicle definitions, for example, in a vehicle definition database. Thevehicle definition database may be any suitable storage location, andmay in some embodiments include a cloud storage device such that thevehicle definition database may be accessed by a plurality of computingdevices (e.g., vehicle user computing devices, such that vehicle usersmay update and/or add vehicle definitions; vehicle computing devices,such that vehicle definitions may be updated based upon sensor data fromthe vehicles).

The VRA computing device may receive each task definition, for example,from one or more task sources. Task sources may be corporate entities(e.g., UBER, LYFT, AMAZON, smaller companies, etc.) that register aplurality of tasks with the vehicle routing system. Task sources mayadditionally or alternatively be individuals that register individualtasks with the vehicle routing system (e.g., requests for individualrides, requests for individual package delivery). The VRA computingdevice may store the task definitions, for example, in a task definitiondatabase. The task definition database may be any suitable storagelocation, and may in some embodiments include a cloud storage devicesuch that the task definition database may be accessed by a plurality ofcomputing devices (e.g., computing devices associated with task sources,such that users may update and/or add task definitions). The taskdefinition database may be similar to and/or integral to the vehicledefinition database. The vehicle definition database and/or the taskdefinition database may be integral to the VRA computing device or maybe remotely located with respect thereto.

The VRA computing device may additionally incorporate ambient data intoits generation of optimal routes for vehicles. Ambient or environmentaldata may be received from vehicles registered with the vehicle routingsystem and/or may be received/retrieved/accessed from one or morethird-party sources. Ambient data may include, for example, one or moreof weather data, traffic data, construction/road closure data, emergencydata, crime data, and/or risk/liability data.

The VRA computing device may apply artificial intelligence, machinelearning, and/or deep learning to any data described herein to developrouting strategies that maximize revenue on a per-vehicle and/orper-vehicle-fleet basis, and/or that optimize use of a vehicle and/or avehicle fleet. In some embodiments, the VRA computing device may developrouting strategies that take into account a most optimal class orcapacity of vehicle available for a task, closest vehicle, bestavailability (e.g., local, regional, domestic, any, etc.), and/orgreatest revenue opportunity.

Additionally or alternatively, the VRA computing device may generateoptimal routes based upon the availability of a plurality of vehicles.For example, the VRA computing device may determine that many vehiclesare available to accept a set of tasks, but that some of the tasks havespecific requirements that are only matched by a subset of the availablevehicles. Accordingly, the VRA computing device may assign morerestrictive tasks first and then assign less restrictive tasks basedupon the remaining vehicle availability. In some embodiments, the VRAcomputing device may only perform such assignments among vehicles withina vehicle fleet, such that availability and revenue opportunity aredistributed throughout the vehicle fleet.

Additionally or alternatively, the VRA computing device may identifylocations and/or classes of cargo for vehicles to service, such aslocations with less competition and/or underserved locations (e.g.,locations with a lowest number of completed tasks). The VRA computingdevice may provide recommendations to vehicle users to expand or modifythe availability radius of their associated vehicles, such that thevehicles may operate in underserved locations.

The VRA computing device may also be configured to leverage eventdata—such as data gathered from publicly available social media, crawledfrom the internet, and/or captured from calendars of vehicle usersregistered with the vehicle routing system—to generate, enhance, modify,and/or update routing strategies. For example, the vehicle routingservice may identify a local event (e.g., a concert or sporting event)taking place at a first location at a first time (and/or within a firsttime period, e.g., from 1 PM-4 PM). The vehicle routing service maydetermine that there are likely to be many potential tasks to completeassociated with the event, such as transporting persons to and from theevent. Specifically, although no or few tasks may already be available(e.g., based upon scheduled or reserved transportation), the VRAcomputing device may determine (e.g., based upon historical event dataand/or data associated with past completed tasks) the event is likely togenerate a plurality of potential tasks, and may route vehicles to theevent at the start and/or end date. The vehicles will be available toaccommodate any potential tasks at the event.

The VRA computing device may be further configured to apply artificialintelligence and/or deep learning to identify and utilize patternsand/or trends in developing routing strategies. In the exemplaryembodiment, the VRA computing device may identify trends and/or patternsbased upon the timing of available and/or completed tasks, such asdaily, weekly, work-weekly (e.g., Monday through Friday), weekend,monthly, yearly, seasonal, and/or other patterns and trends. The VRAcomputing device may additionally or alternatively identify trendsand/or patterns in classes of cargo, types of tasks, and/or locations(e.g., greater number of flower deliveries in February, greater numbersof food deliveries in to particular neighborhoods, greater number ofreturns to a corporate warehouse location). The VRA computing device maydevelop routing strategies that strategically and pre-emptively directvehicles to various locations to accommodate these trends and/orpatterns, making the vehicles available to complete tasks in strategiclocations and/or at strategic times.

It is contemplated that the characteristics of tasks associated withcargo including persons (e.g., ride sharing or other scheduled personaltransportation) may be different from the characteristics of tasksassociated with cargo including objects (e.g., package delivery, fooddelivery, etc.). For example, tasks associated with person(s) as cargo(referred to herein as “person-based tasks”) may, in some cases, presenta higher revenue opportunity than tasks associated with object(s) ascargo (referred to herein as “object-based tasks”), as people may bewilling to pay more to transport themselves than to send packages.

Moreover, the characteristics of person-based tasks may vary widely. Forexample, tasks associated with transporting children, disabled persons,elderly persons, and/or “high-value” persons (e.g., users willing to paymore for privacy, speed, security, etc.) may present a higher revenueopportunity. Likewise, the characteristics of object-based tasks mayvary widely. For example, tasks associated with transporting fragile,high-risk, and/or high-value object may present a higher revenueopportunity. On the other hand, higher-revenue tasks may present greaterrisks and/or restrictions, such as requiring the cargo to be the onlycargo on-board the vehicle, requiring more precise pick-up and/ordelivery timing, requiring faster delivery, requiring human signature orother confirmation of pick-up and/or delivery, requiring certain vehicleor user attributes, and the like, which may limit the availability ofthe revenue opportunity.

In certain embodiments, tasks may offer bonuses or other incentives. Forexample, as described above, certain persons may define their tasks tohave a high value, to incentivize faster, more secure, more premium,and/or more private service. A person may prioritize (and thereforincentivize, or offer a higher task value to) certain types of vehicles,such as preferred autonomous vehicles over manually driven vehicles (orvice versa), vehicles featuring more safety features or amenities, andthe like. In other words, a task source may encourage their associatedtask to be prioritized by increasing the task value, which may cause theVRA computing device to schedule the task more promptly. Additionally oralternatively, users may pay premiums or subscription fees to have theirassociated tasks prioritized by the VRA computing device.

The VRA computing device may further account for various costs indetermining the optimal route for each vehicle. For example, the VRAcomputing device may determine that the fuel costs for one vehicle maybe higher than for another vehicle, and accordingly a particular taskpresents a greater revenue opportunity for the vehicle with lower fuelcosts. Additionally or alternatively, the VRA computing device mayfurther account for other factors in determining the optimal route foreach vehicle. For example, the VRA computing device may determine thatmaintenance costs for one vehicle may be higher than for anothervehicle, and accordingly a particular task presents a greater revenueopportunity for the vehicle with lower maintenance costs. As anotherexample, the VRA computing device may consider an environmental impactof certain routes and/or certain vehicles. The VRA computing device mayprioritize vehicles with reduced environmental impact.

Accordingly, the VRA computing device is configured to executeartificial intelligence, machine learning, and/or deep learningfunctionality to optimally organize and schedule the varying taskswithin the routes of many vehicles. The artificial intelligence and/ormachine or deep learning functionality, in the exemplary embodiment,apply one or more optimization rules that account for the variouscharacteristics of each task as well as the vehicle definition of eachvehicle to generate an optimal route for each vehicle that schedules aplurality of tasks for the vehicle to complete.

The VRA computing device may further account for user preferences orsettings (e.g., stored as part of the vehicle definition) in generatingthe optimal routes. The preferences may include, for example, whetherthe vehicle is available for only person-based tasks, only object-basedtasks, and/or for person- and object-based tasks. Moreover, the VRAcomputing device may analyze the routes developed for vehiclesassociated with only person-based tasks or only object-based tasks andcompare the revenue of those routes to the revenue of more flexibleroutes (e.g., for vehicle that accept both persons and objects ascargo). The VRA computing device may recommend that a vehicle userchange their preferences if the VRA computing device determines, basedupon such a comparison, that the vehicle could be generating morerevenue if the vehicle accepted both persons and cargo.

As another example, the VRA computing device may analyze userpreferences such as vehicle availability, and determine that a vehicleis available for a certain number of hours in a day. The VRA computingdevice may further determine that the vehicle could be generating morerevenue if the vehicle were available for more hours in the day, or ifthe vehicle were available on a different schedule (e.g., if the vehiclewere available during busier time periods).

In addition, the VRA computing device may be configured to detect, inthe identified patterns and/or trends, that there are certain times ofday, certain days, or other times during which vehicles generally (e.g.,on average) complete relatively few tasks, or a number of tasks below athreshold. These times may be referred to as “down times” or “lulltimes.”

The VRA computing device may recognize that these times may beadvantageous for completing tasks that are less profitable or have noprofit associated therewith. For example, the VRA computing device mayschedule service or maintenance for a vehicle during a lull time, andmay route the vehicle to a service vendor to perform the service duringthe lull time.

As another example, the VRA computing device may identify certain“non-profit” or “social good” tasks, which generate little or no profitbut which are associated with service to a local community, such astransporting low income persons who cannot afford to pay for a trip,transporting persons objects to/from non-profit entities (e.g., foodbanks, homeless shelters), and/or transporting the elderly orhandicapped, transporting or delivering medications and needed medicalequipment and the like. The VRA computing device may schedule suchsocial good tasks during lull times. Additionally or alternatively, eachvehicle and/or vehicle fleet may be required or recommended to perform apredetermined number of social good tasks within a period of time (e.g.,monthly, yearly, etc.). The VRA computing device may generate a routefor a vehicle that prioritizes social good tasks, even during moreprofitable times of the day, in order to reach the required orrecommended number of social good tasks. In some embodiments, socialgood tasks may have a low value or no value (with respect to potentialrevenue) but may be have a positive tax incentive that supplements thelow/no value.

“Route continuation,” as used herein, refers generally to division of atask into portions for completion by separate vehicles. The VRAcomputing device may implement route continuation for tasks that areassociated with a long distance between pick-up and delivery (e.g., ifno single vehicle has an appropriate availability radius, if the task ismore profitable if divided, and/or if the task will be completed in ashorter time if divided). For example, cargo that is to be picked up inChicago and delivered to Boston may be subject to route continuation. Afirst vehicle may pick up the cargo in Chicago and deliver the cargo toa second vehicle at a second location intermediate Chicago and Boston(e.g., Cleveland). The second vehicle may pick up the cargo at thesecond location and may deliver the cargo to the delivery location(e.g., Boston) or may deliver the cargo to a third vehicle at a thirdlocation intermediate the second location and Boston (e.g., Syracuse).The third vehicle may pick up the cargo at the third location and thendeliver the cargo to the delivery location.

In the exemplary embodiment, the vehicles registered with the vehiclerouting system capture data during operation thereof (whether thevehicles are autonomous, semi-autonomous, and/or manually driven).Specifically, the vehicles have one or more sensors disposed thereon,such as location sensors, audio sensors, video sensors, cameras, LIDAR,RADAR, GPS/navigation systems, acceleration/deceleration sensors,braking sensors, turning sensors, scanners, and/or any other sensor,including those described elsewhere herein. The sensors operate andcollect and/or generate sensor data passively and/or actively as thevehicle operates (e.g., drives during/between tasks). In someembodiments, the sensor data includes information captured about therespective vehicle's operation, the environment around the vehicle(e.g., infrastructure, weather, etc.), and objects around the vehicle(e.g., other vehicles, people, etc.). The VRA computing device isconfigured to receive and/or access sensor data from the vehiclesregistered with the vehicle routing system. The VRA computing device maystore received sensor data in a sensor data database, which may includea cloud storage database. The sensor data database may be similar toand/or integral to the vehicle definition database and/or the taskdefinition database, as described herein. The VRA computing device mayuse the sensor data in a variety of ways, such as incorporating thesensor data into the intelligent vehicle routing and generating variousreports based upon the sensor data.

In some embodiments, the VRA computing device processes the sensor datato develop vehicle analytics including risk profiles of routes beingtravelled by the vehicle, the vehicle itself, and/or the cargo beingtransported/delivered. For example, the VRA computing device uses sensordata to understand real-time risks and liabilities to vehicle(s),people, interstates, streets, roadway technology and roadway lights,buildings, houses, and landscapes about the vehicles. The risk profilesmay incorporate infrastructure risks and/or liabilities (e.g., routeswith a significant number of potholes or associated with significantnumber of emergency incidents such as crimes and/or accidents), crimedata, real-time environmental data (e.g., weather and/or traffic data),as well as data about the performance of the vehicle during operationthereof. The VRA computing device may use generated risk profiles todetermine appropriate cargo that can be delivered by the vehicle and/orto analyze insurance policies associated with the vehicle, as describedfurther herein. The risk profiles may be real-time risk profiles, whichincorporate real-time ambient data to identify a local, real-time riskfor a particular vehicle, which may impact availability to completetasks, elevate risk to the vehicle/vehicle user, or elevate risk ofinsurance claims activity.

In some embodiments, the VRA computing device may analyze the riskprofiles to determine whether enhanced insurance policies should bepushed to a vehicle user associated with a vehicle experiencing highrisk. For example, the VRA computing device may access an insurancepolicy associated with the vehicle and/or the vehicle user (e.g., froman insurance server/database) and identify whether the retrievedinsurance policy would provide adequate coverage for the level of riskexperienced by the vehicle. If not, the VRA computing device maytransmit a notification to the vehicle user associated with the vehicleof the potential deficiency in their current insurance policy. Thenotification may include a recommendation of an improved insurancepolicy and/or a one-time insurance package associated with a particulartask and/or route.

Additionally or alternatively, the VRA computing device may avoidscheduling tasks in a vehicle's optimal route if the risk associatedwith the task and/or the route thereto/therefrom would exceed a riskthreshold defined in an insurance policy associated with the vehicle.The VRA computing device may notify a vehicle user that because theirinsurance policy defines a particular low risk level, the vehicle isunable to accommodate higher-risk (and, in some cases, higher-value)tasks. The VRA computing device may recommend the vehicle user upgradetheir insurance policy such that the vehicle may be assigned suchhigher-risk but higher-value tasks.

In some embodiments, the VRA computing device may use the sensor data togenerate reports for various entities, such as local governments,businesses, consumers, and/or residents. For example, the reports mayinclude processed and parsed sensor data to highlight infrastructureissues, changes, technology and construction progress and upgrades, workneeding to be performed or maintained, crime, business and residentialconsumer risks and liabilities. The reports may further includerecommendations, solutions, strategies, and/or services to reduce therisks, liabilities, and costs identified in the reports, reduceinsurance costs, and/or to improve property values and revenuepotential.

The VRA computing device may additionally or alternatively leveragevideo, audio, and/or navigation/location sensor data in response tolocal emergencies (e.g., accidents, crime, etc.). This sensor data mayincrease success of responding to emergencies, accidents, disasters orfinding missing persons (e.g., in response to amber alerts, silveralerts, etc.), persons of interest (e.g., wanted criminals), crimes inprogress, fires, and/or cries for help. In some embodiments, in responseto an emergency alert, a vehicle in an affected area may activatevarious sensors and/or transmit real-time sensor data to the VRAcomputing device. The VRA computing device may parse the sensor data andprovide, where applicable, various reports, alerts, data evidence, andthe like to first responders and/or other suitable entities.

In addition, the VRA computing device may incorporate the use of thevehicle's sensors into the operation of the vehicle according to anoptimal route. In the exemplary embodiment, the vehicles include sensorsconfigured to track check-in/check-out of cargo to/from the vehicle,such as scanners, cameras, motion sensors, presence sensors, and thelike. Cargo, such as objects, may have RFID tags, bar codes, and/orother identifiers that may be scanned and/or otherwise sensed by thesensors to track the check-in/check-out of the cargo. Other cargo, suchas persons, may have user computing devices such as smart phones thatthe persons may scan or otherwise present to the vehicle whenentering/exiting the vehicle. Additionally or alternatively, the usercomputing devices may include near-field communication (NFC), RFID,and/or BLUETOOTH functionality that enables check-in/check-out. In someembodiments, a person may have a dedicated device used forcheck-in/check-out, such as a key fob, that may be scanned or otherwisesensed by the vehicle during check-in/check-out. Additionally oralternatively, the vehicle may include one or more biometric sensors,such as facial scanners, fingerprint sensors, and the like, which enableknown persons to securely enter and exit the vehicle.

The vehicle may generate sensor data indicative of thecheck-in/check-out activity at the vehicle. The VRA computing device mayreceive such sensor data from the vehicle and may monitor the sensordata for adherence to the optimal route schedule. For example, the VRAcomputing device may generate and maintain a ledger of allcheck-in/check-out activity, which may be compared to a similar ledgermaintained at the vehicle itself, for data validation purposes. In someembodiments, the VRA computing device and/or the vehicle may maintain aledger as a blockchain, enhancing data security and integrity. Inaddition, the VRA computing device may use the check-in/check-out datato identify when a task is completed and, therefore, to initiate paymentof the task's value to a financial account associated with the vehicle,as described further herein.

Moreover, the VRA computing device may use the sensor data, as well asthe vehicle and task definitions, to develop onboarding strategies for avehicle that optimize the use of space/capacity within the vehicle. Forexample, the VRA computing device may determine that, based upon currentcargo in the vehicle, the vehicle has 50% cargo capacity remaining. TheVRA computing device may additionally determine that tasks having largercargo and/or cargo to be transported further should be arranged withinthe vehicle in a particular way (e.g., larger packages further backand/or closer to the bottom), and may direct one or more human operators(e.g., placing objects into the vehicle) how to optimally arrange thecargo within the vehicle, based upon the optimal route and the sensedavailable space/capacity.

Additionally or alternatively, the VRA computing device may monitor thesensor data and detect a deviation from the optimal schedule, such as anunexpected and/or unauthorized check-in and/or check-out. The VRAcomputing device may initiate one or more corrective measures upondetecting a deviation from the optimal route. For example, if anunauthorized person attempts to enter the vehicle, or if an unauthorizedremoval of an object is detected, the VRA computing device may transmita control signal to the vehicle to (i) generate an audible and/or visualalarm, (ii) activate one or more audio/video sensors to begin recordingto capture evidence of the unauthorized entry/departure, and/or (iii)transmit a report to a first responder.

The VRA computing device may leverage additional sensor data to identifyhigh-risk cargo, which may require additional insurance coverage or mayneed to be rejected from the vehicle. For example, the vehicle mayinclude radiation sensors, cargo scanning sensors, and/or other sensorsthat may generate sensor data used by the VRA computing device to detecthigh-risk and/or dangerous object-based cargo, such as drugs, bombmaking materials, radioactivity, etc. Additionally or alternatively, thevehicle may include audio and/or video sensors, cameras, microphones,and the like that may generate sensor data used by the VRA computingdevice to detect high-risk person-based cargo, such as persons that areunder the influence of drugs or alcohol, that use threatening languageor exhibit violent behavior, and the like. Upon detection of suchhigh-risk cargo, the VRA computing device may (i) transmit a controlsignal to the vehicle to suspend further operation and/or (ii) transmita request for assistance to a first responder (e.g., the police,emergency medical services, etc.).

The VRA computing device may additionally leverage vehicle sensor datato identify real-time environment risks, such as accidents, disasters,extreme weather, terrorism, and the like, and automatically re-route thevehicle away from the risks (e.g., by modifying an optimal route and/ortransmitting override instructions).

The VRA computing device may also process sensor data from vehicles toidentify performance issues associated therewith, which may require thevehicle to be serviced. It should be understood that performance issuesand/or related services may be minor or routine, such as filling a gastank when gas is determined to be low, charging a battery when a batterycharge is determined to be low, changing the oil when the oil level islow or a threshold number of miles have been travelled, and/orinterior/exterior detailing. The performance issues and/or relatedservices may be more acute, such as a flat tire needing to be replaced,a damaged transmission system, required brake maintenance, etc. When theVRA computing device determines the vehicle needs to be serviced, theVRA computing device determines an optimal time for the services to beperformed, as described above, and routes the vehicle to a servicevendor.

Moreover, the VRA computing device may detect from the sensor data thata vehicle is disabled (or is close to being disabled) and requiringreal-time/roadside/emergency service. The VRA computing device maytransfer any incomplete tasks from the vehicle to another vehicle. Forexample, the VRA computing device may modify optimal routes of one ormore other vehicles to take on future tasks and/or travel to thedisabled vehicle to onboard any cargo that was being transported in thedisabled vehicle. Additionally, the VRA computing device may request aservice vehicle travel to the disabled vehicle, for example, from aservice vendor, and request that the service vehicle tow or otherwisetransfer the disabled vehicle to a service location for service/repair.

In some embodiments, the VRA computing device initiates and/orfacilitates payment for completed tasks to financial accounts associatedwith vehicle users. The VRA computing device, as described herein,determines when a task is complete, identifies the vehicle thatcompleted the task, and identifies a task value associated with thecompleted task (e.g., by retrieving or accessing the task definition ora cached copy thereof). The VRA computing device further identifies auser or user account associated with the task, which may include anindividual person, a corporate account, etc. The VRA computing devicedetermines a financial institution associated with the user/user account(a “task financial institution”), as well as a financial institutionassociated with the vehicle user of the vehicle that completed the task(a “vehicle financial institution”). The VRA computing device transmitsan instruction to the task financial institution to transfer funds froma payment account of the user associated with the task to a paymentaccount of the vehicle user. The VRA computing device may facilitatetask value division, provision of bonuses/incentives from users,provision of tax incentives, and/or any other financial servicesassociated with the completion of tasks as described herein. The VRAcomputing device may transmit funds-transfer instructions for payment ofcompleted tasks after each individual task is completed, periodically,in batches, etc.

In some embodiments, the VRA computing device enables investment invehicles and/or vehicle fleets registered with the vehicle routingsystem. The VRA computing device enables investors to contribute orwholly invest in the purchase and/or management of vehicles and/orvehicle fleets. The VRA computing device manages payment to investorparties in accordance with their relative investment. For example, aninvestor invests $X and receives a Y % return. Investors mayadditionally, in some embodiments, receive credits for fees and/or taskvalues. Additionally or alternatively, the VRA computing devices enablesinvestors to invest in vehicles and/or vehicle fleets by pre-paying fordelivery services by vehicles and/or vehicle fleets registered with thevehicle routing system.

In the exemplary embodiment, the VRA computing device also providesmanagement facilitation services to users thereof (both task sources andvehicle users). Specifically, the VRA computing device maintains amanagement hub software application or “app” that enables vehicle usersand task sources to track various metrics, adjust user settings, andaccess a plurality of services associated with the vehicle routingsystem. The management hub app may be executed on user computing devicesand/or in-vehicle computing devices, as described further herein.

In one embodiment, the management hub app enables a vehicle user to viewmetrics for the vehicle(s) associated with the vehicle user. Forexample, for each vehicle and/or for an entire vehicle fleet (or asubset thereof), the management hub app displays optimal routes,performance/service status, vehicle usage, location, revenue generated,risk ratings, business reports, and/or recommendations (e.g., to improverevenue, improve performance, reduce risk, reduce costs, etc.). Suchinformation may be “snapshots” of real-time metrics and/or reports thatdisplay and/or describe the metrics over a period of time.

The management hub app may further enable the vehicle user to adjust oneor more settings, such as user preferences associated with one or morevehicles, and/or other data elements of the vehicle definitions. Thevehicle user may additionally or alternatively add or delete vehiclesfrom an account, may add or delete other vehicle users (e.g., may add“manager” users that monitor the vehicle metrics), may modify paymentaccount information associated with one or more vehicles, and adjustother settings and/or preferences.

Likewise, the management hub app may enable task sources to view metricsassociated with their registered tasks, such as tasks in progress, arelative/average priority of their tasks, a percentage of taskscompleted, an average risk rating (or other score/rating) associatedwith their tasks, and the like. The task sources may add or deletetasks, modify payment account information associated with one or moreuser accounts, add or modify subscription/priority settings, and adjustother settings and/or preferences. The management hub app may furtherenable users thereof to view service vendors and/or other partners ofthe vehicle routing system (e.g., preferred dealerships, detailers,mechanics, etc.), and may enable vehicle users to prioritize servicevendors and/or request particular services for their vehicles (e.g.,detailing).

The management hub app may further include various financialcapabilities. For example, the management hub app may enable users toview cost-revenue comparisons for one or more vehicles, reviewrecommendations to generate additional revenue, review service costs,review incoming and/or outgoing task-value payment(s), invest invehicles and/or vehicle fleets, view investment results, pre-pay fortasks or purchase subscriptions to the vehicle routing system, and thelike. In some embodiments, the management hub app enables corporate orbusiness manager vehicle users to review metrics associated withemployees that drive their registered vehicle and may modify ordistribute payments and/or payments through the management hub app. Themanagement hub app may include insurance functionality, such that usersmay view current insurance policies, modify (e.g., upgrade or enhance)current insurance policies, purchase one-time or on-demand insurancepolicies for certain tasks, submit claims, and the like.

The management hub app may include various other functionalities, forexample: (i) enabling users to view and/or manage legal or financialdisputes, (ii) enabling users (e.g., small business users) to purchaseadvertising or marketing services, and/or (iii) review public and/orprivate ratings of vehicles, task sources, and/or vehicle users.

The VRA computing device may include one or more databases to store anydata, such as vehicle definitions, task definitions, sensor data,optimal routes, check-in/check-out ledgers, and/or any other datadescribed herein (e.g., user data, insurance data, digital communicationdata, financial data, usage data, etc.). The databases may be anysuitable database, including cloud storage databases, which enablesecure storage of such data as well as secure data access by one or morecomputing devices.

The methods and system described herein may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware, or any combination or subset. As disclosedabove, at least one technical problem with current systems forrequesting deliveries and completing tasks rely on the users to choosetheir tasks and frequently restrict concurrent tasks. Specifically,these current systems do not have automatic task selection and/orrouting capabilities. Accordingly, vehicles may be used inefficiently,vehicle users may be deprived of additional revenue, and/or tasks maynot be completed timely. Moreover, current systems are not able toaccess and/or leverage data from the vehicles in suggesting ordistributing tasks, nor in making recommendations to vehicle users. Thesystem and methods described herein address these technical problems.

The technical effect of the systems and processes described herein maybe achieved by performing at least one of the following steps: (a)retrieving a vehicle definition for the vehicle, the vehicle definitionincluding availability parameters and delivery preferences associatedwith the vehicle; (b) retrieving, based in part on the vehicledefinition, a plurality of task definitions defining a respectiveplurality of tasks, each task including a respective cargo to bedelivered, pick-up time, delivery time, pick-up location, deliverylocation, and task value; (c) generating, by executing at least one ofartificial intelligence and deep learning functionality using thevehicle definition and the plurality of task definitions, an optimalroute for the vehicle that includes a scheduled list of a subset of theplurality of tasks for the vehicle to perform, wherein the optimal routemaximizes the potential revenue for operation of the vehicle within aperiod of time associated with the optimal route; (d) transmitting theoptimal route to the vehicle for operation of the vehicle according tothe optimal route; (e) receiving, from a first vehicle, sensor dataduring operation of the first vehicle according to the optimal route;(f) processing the received sensor data to generate vehicle analyticsassociated with a performance of the vehicle; (g) generating one or morevisual representations of the vehicle analytics; and/or (h) transmittingthe one or more visual representations of the vehicle analytics to acomputing device of the vehicle user for display within a user interfaceof a management hub application executed on the user computing device

The resulting technical effect is that tasks may be optimallydistributed between available vehicle, and vehicles may be used moreefficiently by completing an optimal number of tasks in an optimal orderthat maximizes revenue generation for vehicle users. A solution to theabove-described problems provided by the vehicle routing system is thedevelopment of optimal routing strategies based upon the characteristicsof tasks and available vehicles. The vehicle routing system may employartificial intelligence and/or deep learning functionality to generateoptimal routes, which increases the flexibility of the system to respondto changes in tasks, environment, risk, and/or other factors to generatethe optimal routes. In particular, the vehicle routing system is wellsuited to incorporating sensor data received from registered vehicles(e.g., vehicle analytics) into various rules or algorithms of theartificial intelligence and/or deep learning functionality. The vehiclerouting system also provides a management hub application (“app”) thatenables vehicle users to view and manage optimal routes and vehicleanalytics, and to manage various other elements of their relationshipwith the vehicle routing system.

Exemplary Vehicle

FIG. 1 depicts a view of an exemplary vehicle 100. In the exemplaryembodiment, vehicle 100 is an autonomous or semi-autonomous vehiclecapable of fulfilling the transportation and delivery capabilities asdescribed in further detail herein. In these embodiments, vehicle 100may be capable of sensing its environment and navigating without humaninput. In other embodiments, vehicle 100 is a manual vehicle, such as atraditional automobile that is controlled by a driver (not shown).

Vehicle 100 may include any kind of vehicle, such as, for example, cars,trucks, all-terrain vehicles (ATVs), motorcycles, bicycles, recreationalvehicles (RVs), snowmobiles, boats, industrial vehicles (e.g.,construction vehicles), “riding” lawnmowers, smart farming equipment,ships, planes, helicopters, and so forth. Generally, vehicles 100 willbe described herein using cars/trucks (e.g., personal vehicles) asexamples. However, these examples should not be construed to limit thedisclosure in any way, as the scope of the present disclosure may beapplicable to any kind of autonomous vehicle, including those listedhereinabove.

Vehicle 100 may be capable of sensing aspects of its environment and, insome cases, assisting in or performing control aspects associated withpiloting vehicle 100 (e.g., via automation systems 104, with or withouthuman input). Vehicle 100 may include a plurality of sensors 106. Theplurality of sensors 106 may detect the current surroundings andlocation of vehicle 100. Plurality of sensors 106 may include, but arenot limited to, radar, LIDAR, GPS receivers, video devices, imagingdevices, cameras, audio recorders, and computer vision.

Plurality of sensors 106 may also include sensors that detect conditionsof vehicle 100, such as speed, acceleration, gear, braking, and otherconditions related to the operation of vehicle 100, for example: atleast one of a measurement of at least one of speed, direction, rate ofacceleration, rate of deceleration, location, position, orientation, androtation of the vehicle, and a measurement of one or more changes to atleast one of speed, direction, rate of acceleration, rate ofdeceleration, location, position, orientation, and rotation of thevehicle. Furthermore, plurality of sensors 106 may include impactsensors that detect impacts to vehicle 100, including force anddirection, and sensors that detect actions of vehicle 100, such thedeployment of airbags.

In some embodiments, plurality of sensors 106 may detect the presence ofcargo 108 in vehicle 100. In some embodiments, cargo 108 includespersons, such as one or more passengers (not specifically shown) invehicle 100. In these embodiments, plurality of sensors 106 may detectthe presence of fastened seatbelts, the weight in each seat in vehicle100, heat signatures, or any other method of detecting information aboutthe passengers in vehicle 100. In some embodiments, cargo 108 includesitems, such as packages or other objects, which may be located in acabin of vehicle 100 and/or in a trunk of vehicle 100. In theseembodiments, plurality of sensors 106 may include scanners (e.g., RFIDscanners, optical scanners, tag readers), weight sensors, cameras,and/or other suitable sensor that may detect the presence and/orposition of cargo 108, introduction of cargo 108 into/onto vehicle 100,and/or removal of cargo 108 from vehicle 100 (e.g., check-in and/orcheck-out of cargo 108).

In these embodiments, vehicle 100 includes an in-vehicle computingdevice 102 configured to manage aspects of autonomous vehicle operationprovided by a plurality of automation systems 104, each of whichrepresent an electronic control system onboard vehicle 100 that may beinvolved in some aspect of piloting vehicle 100. As described furtherherein, in-vehicle computing device 102 may be configured to operatevehicle 100 according to routing instructions from a centralized vehiclerouting and analytics (VRA) computing device 130.

Automation systems 104 may interpret the sensory information fromsensors 106 while performing various operations. Automation systems 104may include, for example, (a) fully autonomous (e.g., driverless)driving; (b) limited driver control; (c) vehicle-to-vehicle (V2V)wireless communication; (d) vehicle-to-infrastructure (and/or viceversa) wireless communication; (e) automatic or semi-automatic steering;(f) automatic or semi-automatic acceleration; (g) automatic orsemi-automatic braking; (h) automatic or semi-automatic blind spotmonitoring; (i) automatic or semi-automatic collision warning; (j)adaptive cruise control; (k) automatic or semi-automatic parking/parkingassistance; (l) automatic or semi-automatic collision preparation(windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m)driver acuity/alertness monitoring; (n) pedestrian detection; (o)autonomous or semi-autonomous backup systems; (p) road mapping systems;(q) software security and anti-hacking measures; (r) theftprevention/automatic return; (s) automatic or semi-automatic drivingwithout occupants; and/or other functionality. While not all sensortypes for each particular automation system 104 are listed here, itshould be understood that sensors 106 include any sensors sufficient toallow the associated automation system 104 to operate for its intendedpurpose. As such, each particular automation system 104 may utilize somedata from sensors 106 to perform its underlying function.

In some embodiments, vehicle 100 may include a user interface (notshown) such that vehicle users of vehicle 100 may access certainfeatures of vehicle 100 (e.g., receive alerts from automation systems104 or in-vehicle computing device 102, review or adjust routinginstructions from VRA computing device 130, etc.). The user interfacemay, in some embodiments, be integral to in-vehicle computing device102. In-vehicle computing device 102 may be configured to collect sensordata from sensors 106. In some embodiments, in-vehicle computing device102 may process, store, and/or transmit sensor data, including asdescribed herein.

In-vehicle computing device 102 may be any computing device capable ofperforming the functions described herein. In-vehicle computing device102 may be integral to vehicle 100 (e.g., a console computing device)and/or may be coupled to vehicle 100 (e.g., an after-market or retro-fitcomputing device). Moreover, it is contemplated that in someembodiments, in-vehicle computing device 102 may additionally oralternatively include a user computing device (not shown in FIG. 1)communicatively coupled or “paired” with vehicle 100 (e.g., via aBluetooth® connection). In-vehicle computing device 102 may collect,compile, format, and/or otherwise process sensor data for transmissionto VRA computing device 130. Additionally or alternatively, in-vehiclecomputing device 102 may facilitate tracking and monitoring check-in andcheck-out of cargo 108 to and from vehicle 100, such as by facilitatingoperation of one or more scanners, biometric sensors, and the like.In-vehicle computing device 102 may control operation of automationsystems 104 for operation of vehicle 100, for example, based upon anoptimal route from VRA computing device 130, one or more control signalsform VRA computing device 130, and/or additional or alternativeinstructions. In-vehicle computing device 102 may store or cache sensordata from sensors 106, or compressed/condensed/abbreviated versionsthereof, such that in-vehicle computing device 102 may maintain acheck-in/check-out ledger and/or any other suitable sensor data recordsthat track operation of vehicle 100.

Vehicle 100 may include multiple communication devices 112 forconnecting to multiple different types of networks. Communicationdevices 112 may include, for example, a wired or wireless networkadapter and/or a wireless data transceiver for use with a mobiletelecommunications network. Communication devices 112 may be configuredto communicate using many interfaces including, but not limited to, atleast one of a network, such as the Internet, a local area network(LAN), a wide area network (WAN), or an integrated services digitalnetwork (ISDN), a dial-up-connection, a digital subscriber line (DSL), acellular phone connection, a cable modem, a Wi-Fi connection, and aBluetooth® connection.

Vehicle 100 includes an onboard communications network (“onboardnetwork”) 110 that communicatively couples various electronics andcomputing devices on vehicle. In the exemplary embodiment, onboardnetwork 110 communicatively couples in-vehicle computing device 102,sensors 106, automation systems 104, and communications device(s) 112.In some embodiments, vehicle 100 may be able to communicate with one ormore remote computer devices, such as a VRA computing device 130, viaone or more wireless networks 114, using a communication device 112(e.g., wireless network adapter). Network 114 may include, for example,a cellular network, a satellite network, and a wireless vehicular ad-hocnetwork. In this example, network 114 is a cellular network, perhapsalso connected to the Internet (not separately shown in FIG. 1), thatallows vehicle 100 to communicate with VRA computing device 130.

In some embodiments, VRA computing device 130 may include, or otherwisebe connected to, a one or more database(s) 132. Database 132 may includesuch information as, for example, vehicle definitions, task definitions,sensor data, vehicle usage data, reports, optimal routes, and the like.In some embodiments, database 132 may be accessed by one or morecomponents of vehicle 100, such as, for example, in-vehicle computingdevice 102 or automation systems 104.

Exemplary Vehicle Routing System for Intelligently Routing Vehicles toComplete Tasks and Maximize Revenue

FIG. 2 depicts a schematic diagram of an exemplary vehicle routingsystem 200. Vehicle routing system 200 is configured to intelligentlyroute vehicles 100 to complete delivery tasks and maximize deliveryrevenue. In one exemplary embodiment, vehicle routing system 200 mayinclude and/or facilitate communication between one or more vehicles 100(e.g., via in-vehicle computing devices 102 and communication devices112) and VRA computing device 130, and/or between VRA computing device130 and one or more of user computing devices 202, vendor computingdevice 204, third party device 206, insurance servers 208, and/orfinancial institutions 210.

VRA computing device 130 may be implemented as a server computing devicewith artificial intelligence and deep learning functionality.Alternatively, VRA computing device 130 may be implemented as any devicecapable of interconnecting to the Internet, including mobile computingdevice or “mobile device,” such as a smartphone, a “phablet,” or otherweb-connectable equipment or mobile devices. VRA computing device 130may be in communication with vehicles 100, one or more user computingdevice 202, vendor computing devices 204, third party devices 206,insurance servers 208, and/or financial institutions 210, such as viawireless communication or data transmission over one or more radiofrequency links or wireless communication channels. In the exemplaryembodiment, components of vehicle routing system 200 may becommunicatively coupled to the Internet through many interfacesincluding, but not limited to, at least one of a network, such as theInternet, a local area network (LAN), a wide area network (WAN), or anintegrated services digital network (ISDN), a dial-up-connection, adigital subscriber line (DSL), a cellular telecommunications connection,and a cable modem.

Vehicle routing system 200 includes one or more database(s) 132containing information on a variety of matters. For example, database132 may include such information as vehicle definitions, taskdefinitions, vehicle sensor data, vehicle usage reports,check-in/check-out ledgers, generated optimal routes, generatedrecommendations and/or reports, ambient/environmental data, insurancedata (e.g., policies associated with vehicles 100), user data (e.g.,associated with vehicle users and/or task sources), and/or any otherinformation used, received, and/or generated by vehicle routing system200 and/or any component thereof, including such information asdescribed herein. In one exemplary embodiment, database 132 may includea cloud storage device, such that information stored thereon may beaccessed by one or more components of vehicle routing system 200, suchas, for example, VRA computing device 130, in-vehicle computing devices102, and/or user computing devices 202. In one embodiment, database 132may be stored on VRA computing device 130. In any alternativeembodiment, database 132 may be stored remotely from VRA computingdevice 130 and may be non-centralized.

Vehicle routing system 200 includes a plurality of vehicles 100registered therewith. Although only three vehicles 100 are shown in FIG.2, it should be readily understood that any number of vehicles 100 maybe registered with vehicle routing system 200 to receive optimal routesfrom VRA computing device 130. As described above, each vehicle 100 mayinclude any kind of vehicle, such as, for example, cars, trucks,all-terrain vehicles (ATVs), motorcycles, autonomous vehicles,semi-autonomous vehicles, recreational vehicles (RVs), snowmobiles,boats, industrial vehicles (e.g., construction vehicles), “riding”lawnmowers, flying cars, robo-taxis, and/or any kind of vehicle.Generally, vehicles 100 will be described herein using cars/trucks(e.g., personal vehicles) as examples. However, these examples shouldnot be construed to limit the disclosure in any way, as the scope of thepresent disclosure may be applicable to any kind of vehicle, includingthose listed hereinabove.

In the exemplary embodiment, vehicle 100 includes communication device112 such that vehicle 100 may communicate with VRA computing device 130,for example, via the Internet. Vehicle 100 may additionally communicatewith other components of vehicle routing system 200, such as database132, user computing device(s) 202, insurance server 208, etc. Asdescribed herein, vehicles 100 are configured to operate in accordancewith optimal routes generated by VRA computing device 130 in order tomaximize revenue associated with completing delivery tasks. Vehicles 100may be further configured to capture and/or generate sensor dataassociated with operation of vehicles 100, including sensor dataassociated with cargo 108 (shown in FIG. 1), performance of vehicle 100,and associated with an environment 214 about vehicle 100. Environment214 may include weather, infrastructure, obstacles, other vehicles,persons, and/or any other object or occurrence. VRA computing device 130may receive any data from vehicles 100 (e.g., via in-vehicle computingdevice 102 and communication device 112), such as sensor data. VRAcomputing device 130 may transmit any data to vehicle 100, includingoptimal routes, control instructions, modified routes and/or controlinstructions, and the like.

In the exemplary embodiment, user computing devices 202 may be computersthat include a web browser or a software application to enable usercomputing devices 202 to access the functionality of VRA computingdevice 130 using the Internet or a direct connection, such as a cellularnetwork connection. User computing devices 202 may be any device capableof accessing the Internet including, but not limited to, a desktopcomputer, a mobile device (e.g., a laptop computer, a personal digitalassistant (PDA), a cellular phone, a smartphone, a tablet, a phablet,netbook, notebook, smart watches or bracelets, smart glasses, wearableelectronics, pagers, etc.), or other web-based connectable equipment.User computing device 202 may be associated with vehicle users havingvehicle registered with vehicle routing system 200, task sources (e.g.,individual users that register tasks with vehicle routing system 200and/or users with user accounts associated with corporate or businessentities that register tasks with vehicle routing system 200), and/orinvestors. User computing devices 202 may be used to access a managementhub app 212 maintained by VRA computing device 130, for example, via auser interface 216 when management hub app 212 is executed on usercomputing device 202.

In the exemplary embodiment, VRA computing device 130 may be associatedwith vehicle routing and vehicle analytics services provided by vehiclerouting system 200. Users, including vehicle users, tasks sources,investors, and the like, may register or sign up with vehicle routingsystem 200 (e.g., using user computing devices 202) to access thevehicle routing and vehicle analytics functionality of VRA computingdevice 130. VRA computing device 130 may receive any data from users(e.g., via user computing devices 202), such as vehicle definitions,task definitions, insurance data, investment data, financial data, andthe like. VRA computing device 130 may transmit any data to users (e.g.,via management hub app 212 executed on user computing devices 202), suchas recommendations, reports, vehicle analytics, investment results,payment data, and the like. In some embodiments, user computing devices202 may be configured to transmit data to and/or receive data fromvehicles 100. For example, a user computing device 202 of a vehicle userhaving a vehicle 100 may transmit control instructions to vehicle 100directing the vehicle 100 to travel to a particular location or tofollow control instructions from VRA computing device 130.

Vendor computing devices 204 may be computing devices associated withvendors that have a relationship with vehicle routing system 200, suchas providing one or more services to vehicle routing system 200 (e.g.,vehicle maintenance/repairs, detailing, towing/recovery, and the like).Such vendors may be referred to as “partner vendors” and/or “preferredvendors.” VRA computing device 130 may communicate with vendor computingdevice 204, for example, to schedule services for one or more vehicles100, to receive fee schedules from the vendor, to transmit or receivefeedback to/from vendor computing device 204, and/or to transmit and/orreceive any other information. Vendor computing device 204 may be anydevice capable of interconnecting to the Internet, including a servercomputing device, a mobile computing device or “mobile device,” such asa smartphone, or other web-connectable equipment or mobile devices.

Third party devices 206 may be computing devices associated withexternal sources of data, such as sources of ambient data, event data,internet content, social media data, and the like. VRA computing device130 may request, receive, and/or otherwise access data from third partydevices 206. Third party devices 206 may be any devices capable ofinterconnecting to the Internet, including a server computing device, amobile computing device or “mobile device,” such as a smartphone, orother web-connectable equipment or mobile devices.

Insurance server 208 may be associated with and/or maintained by aninsurance provider, which provides insurance policies associated withvehicles 100, cargo 108 (shown in FIG. 1), tasks, vehicle users, and thelike. Insurance server 208 may communicate with VRA computing device130, user computing device(s) 202, and/or database 132 in order totransmit and/or receive information associated with the insurancepolicies.

For example, insurance server 208 may transmit insurance policies to VRAcomputing device 130, and/or may receive requested modification toinsurance policies and/or purchases of supplemental, one-time insurancepolicies from VRA computing device 130 and/or user computing devices202. As another example, insurance server 208 may receive vehicle sensordata, usage data, and/or risk profiles from VRA computing device 130and/or database 132 and may update or recommend updates to an insurancepolicy associated with a vehicle 100 based thereon.

Financial institution 210 may include any financial institutionassociated with any component of vehicle routing system 200, one or morevehicle users, one or more task sources, and/or one or more investors inone or more vehicles 100. For example, financial institution 210 mayinclude a bank, at which one or more payment account(s) is maintained,that payment account(s) associated with any component of vehicle routingsystem 200, one or more vehicle users, one or more task sources, and/orone or more investors in one or more vehicles 100. VRA computing device130 may be in communication with any number of financial institutions210, and may transmit funding requests thereto to pay vehicle users forcompleted tasks.

Exemplary Vehicle Routing and Analytics Computing Device

FIG. 3 depicts a schematic diagram of an exemplary VRA computing device130 (as shown in FIGS. 1 and 2). In one exemplary embodiment, VRAcomputing device 130 may include a processor 302, a memory 304 (whichmay be similar to database 132, also shown in FIGS. 1 and 2), acommunication interface 306, and a storage interface 308. Processor 302is configured to execute instructions, which may be stored in memory304. Processor 302 includes one or more processing units (e.g., in amulti-core configuration) and may be configured to execute a pluralityof modules.

In the exemplary embodiment, processor 302 is operable to execute anartificial intelligence/deep learning (AI/DL) module 310, arouting/analytics module 312, a reports/recommendations module 314, anda module 316 that maintains functionality for management hub app 212(shown in FIG. 2). Modules 310, 312, 314, and 316 may includespecialized instruction sets, and/or coprocessors. Database 132 and/ormemory 304 may store any data and/or instructions necessary for modules310, 312, 314, and 316 to function as described herein. In the exemplaryembodiment, database 132 stores vehicle definitions 320, taskdefinitions 322, and sensor data 324.

AI/DL module 310 may execute artificial intelligence and/or deeplearning functionality on behalf of routing/analytics module 312.Specifically, AI/DL module 310 may include any rules (e.g., routeoptimization rules), algorithms, training data sets/programs, and/or anyother suitable data and/or executable instructions that enable VRAcomputing device 310 to employ artificial intelligence and/or deeplearning to generate optimal routes, generate vehicle analytics,incorporate vehicle analytics to modify rules and/or algorithms, and thelike.

Routing/analytics module 312 may create efficient routing strategies forone or more vehicles (e.g., vehicles 100, shown in FIGS. 1 and 2). Forexample, routing/analytics module 312 may access and process vehicledefinitions 320, task definitions 322, and/or sensor data 324 fromdatabase 132 to generate optimal routes for one or more vehicles.Routing/analytics module 312 may develop routing strategies thatmaximize revenue on a per-vehicle and/or per-vehicle-fleet basis, and/orthat optimize use of a vehicle and/or a vehicle fleet. In someembodiments, routing/analytics module 312 may develop routing strategiesfor a most optimal class or capacity of vehicle available for a task,closest vehicle, best availability (e.g., local, regional, domestic,any, etc.), and/or greatest revenue opportunity. Additionally oralternatively, routing/analytics module 312 may generate optimal routesbased upon the availability of a plurality of vehicles.

In generating optimal routes, routing/analytics module 312 may identifylocations and/or classes of cargo for vehicles to service, such aslocations with less competition and/or underserved locations (e.g.,locations with a lowest number of completed tasks). Routing/analyticsmodule 312 may automatically route vehicles to such underservedlocations, by generating and/or modifying optimal routes and/or bytransmitting control instructions to the vehicles. Routing/analyticsmodule 312 may also be configured to leverage event data (notshown)—such as data gathered from publicly available social media,crawled from the internet, and/or captured from calendars of vehicleusers registered with vehicle routing system 200—to generate, enhance,modify, and/or update routing strategies. For example, routing/analyticsmodule 312 may identify a local event (e.g., a concert or sportingevent) taking place at an event location at a first time and/or within afirst time period (e.g., from a start time to and end time).Routing/analytics module 312 may route vehicles to the event location atthe start and/or end date, by generating and/or modifying optimal routesand/or by transmitting control instructions to the vehicles.

Routing/analytics module 312 may be further configured to employ AI/DLmodule 310 to identify patterns and/or trends, for example, in sensordata 324 and/or in historical data that may also be stored in databaseas part of sensor data 324 and/or separately therefrom (e.g., historicalroutes travelled, historical tasks completed, etc.). In the exemplaryembodiment AI/DL module 310 may identify timing trends and/or patterns;trends and/or patterns in classes of cargo, types of tasks, and/orlocations; and/or any other trends and/or patterns. Routing/analyticsmodule 312 may develop routing strategies that strategically andpre-emptively direct vehicles to various locations to accommodate thesetrends and/or patterns. Routing/analytics module 312 may also developrouting strategies that respond to identified lull times, by schedulingservices and/or social good tasks during lull times.

Routing/analytics module 312 may generate optimal routes based uponcost-benefit analyses for each task, accounting for a task value of eachtask as well as costs associated with completing the task.Routing/analytics module 312 may further generate optimal routes basedupon user preferences or settings (e.g., stored as part of vehicledefinitions 320 and/or task definitions 322).

Routing/analytics module 312 may also generate vehicle analytics basedupon sensor data 324 (which includes data characterizing operation ofthe vehicle according to an optimal route, completion of tasks, etc.).Vehicle analytics may include, for example, risk profiles of vehicles(and/or the routes travelled by vehicles and/or cargo deliveredtherein), check-in/check-out ledgers, cargo placement optimization,performance issues, service needs, and the like.

Report/recommendation module 314 may be configured to generate andtransmit recommendations and/or reports to users, such as vehicle users,task sources, and/or investors. In some embodiments,report/recommendation module 314 provides recommendations such as: (i)recommendations to vehicle users to expand or modify the availabilityradius of their associated vehicles, such that the vehicles may operatein underserved locations; (ii) recommendations to vehicle users tomodify restrictive delivery preferences to generate additional revenue;and/or (iii) other types of recommendations, including those discussedelsewhere herein.

Report/recommendation module 314 may provide reports for variousentities, such as local governments, businesses, consumers, and/orresidents. For example, the reports may include processed and parsedsensor data to highlight infrastructure issues, changes, technology andconstruction progress and upgrades, work needing to be performed ormaintained, crime, business and residential consumer risks andliabilities. The reports may further include recommendations, solutions,strategies, and/or services to reduce the risks, liabilities, and costsidentified in the reports, reduce insurance costs, and/or to improveproperty values and revenue potential.

In the exemplary embodiment, report/recommendation module 314 providesgenerated reports and/or recommendations through management hub app 212.Accordingly, report/recommendation module 314 may employ module 316 toprovide the recommendations and/or reports to user computing devices 202through management hub app 212.

Module 316 is configured to facilitate maintaining management hub app212 and providing the functionality thereof to users. Module 316 maystore instructions that enable the download and/or execution ofmanagement hub app 212 at the user computing devices. Module 316 maystore instructions regarding user interfaces, controls, commands,settings, and the like, and may format data, such as reports,recommendations, analytics, and the like, into a format suitable fortransmitting to user computing devices for display thereat.

In the exemplary embodiment, processor 302 is operatively coupled tocommunication interface 306 such that VRA computing device 130 iscapable of communicating with remote device(s) such as vehicles 100, oneor more user computing device 202, vendor computing devices 204, thirdparty devices 206, insurance servers 208, and/or financial institutions210 (all shown in FIG. 2) over a wired or wireless connection. Forexample, communication interface 306 may receive vehicle definitions 320and/or task definitions 322 form user computing devices 202 and/or mayreceive sensor data 324 from vehicles 100. Communication interface 306may include, for example, a wired or wireless network adapter and/or awireless data transceiver for use with a mobile telecommunicationsnetwork.

Processor 306 may also be operatively coupled to database 132 (and/orany other storage device) via storage interface 308. Database 132 may beany computer-operated hardware suitable for storing and/or retrievingdata. In some embodiments, database 132 may be integrated in VRAcomputing device 130. For example, VRA computing device 132 may includeone or more hard disk drives as database 132. In other embodiments,database 132 is external to VRA computing device 130 and is accessed bya plurality of computer devices. For example, database 132 may include astorage area network (SAN), a network attached storage (NAS) system,multiple storage units such as hard disks and/or solid state disks in aredundant array of inexpensive disks (RAID) configuration, cloud storagedevices, and/or any other suitable storage device.

Storage interface 308 may be any component capable of providingprocessor 302 with access to database 132. Storage interface 308 mayinclude, for example, an Advanced Technology Attachment (ATA) adapter, aSerial ATA (SATA) adapter, a Small Computer System Interface (SCSI)adapter, a RAID controller, a SAN adapter, a network adapter, and/or anycomponent providing processor 302 with access to database 132.

Processor 302 may execute computer-executable instructions forimplementing aspects of the disclosure. In some embodiments, processor302 may be transformed into a special purpose microprocessor byexecuting computer-executable instructions or by otherwise beingprogrammed. For example, processor 302 may be programmed with theinstruction such as those illustrated in FIGS. 9, 10, and 11.

Memory 304 may include, but is not limited to, random access memory(RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory(ROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), and non-volatile RAM(NVRAM). The above memory types are example only, and are thus notlimiting as to the types of memory usable for storage of a computerprogram.

Exemplary Optimal Routing

FIGS. 4A and 4B illustrate exemplary vehicle routes 400 and 450,respectively. More specifically, FIG. 4A illustrates a first vehicleroute 400 that may be generated using current delivery systems. Firstvehicle route 400 may be implemented using a vehicle (e.g., vehicle 100,shown in FIG. 1). In the illustrated embodiment, first vehicle route 400includes two tasks, which may have been selected and scheduled by avehicle user associated with the vehicle. For example, the vehicle maydeliver cargo including a commuting person.

Specifically, the vehicle may pick up a passenger A at a location 1 at8:00 AM, and may drop off passenger A at a location 2 at 9:00 AM, whichrepresents a first task. The vehicle may pick up passenger A at location2 at 5:00 PM and drop off passenger A at location 1 at 6:00 PM, whichrepresents a second task. First vehicle route 400 does not takeadvantage of the time between 9:00 AM and 5:00 PM, in which the vehiclecould be completing additional tasks. Moreover, first vehicle route 400does not enable concurrent tasks, nor tasks associated with cargoincluding objects (e.g., package delivery).

Second vehicle route 450 is an example of an optimal route generatedaccording to the present disclosure, and may be generated by VRAcomputing device 130 (shown in FIG. 1). In the illustrated embodiment,second vehicle route 450 (referred to hereinafter as “optimal route450”) includes at least six tasks (the schedule of tasks is abbreviatedin FIG. 4B and may not show each pair of pick-up and delivery for eachtask) that may be completed throughout a day by the vehicle. Optimalroute 450 (i) takes advantage of a full day's availability of thevehicle, (ii) enables concurrent tasks (e.g., simultaneouslytransporting passenger A and package B), and (iii) enables bothperson-based tasks and object-based tasks. Moreover, optimal route 450may include all of these advantages over first vehicle route 400 whilekeeping the vehicle within substantially the same geographic radius asfirst vehicle route 400.

Where the vehicle is a manually driven vehicle, VRA computing device 130may transmit optimal route 450 to a user computing device (e.g., usercomputing device 202, shown in FIG. 2) of a driver of the vehicle, suchthat the driver may operate the vehicle according to optimal route 450.Where the vehicle is an autonomous vehicle, VRA computing device 130 maytransmit optimal route 450 within a control signal to the vehicle (e.g.,to an in-vehicle computing device of the vehicle, such as in-vehiclecomputing device 102, shown in FIG. 1). The control signal may cause thevehicle to operate according to optimal route 450.

FIG. 5 illustrates an exemplary task 500 subject to route continuation.More specifically, task 500 has been divided into three portions (I, II,III), where each portion is scheduled in an optimal route generated foreach of three vehicles 100 (Car 1, Car 2, and Car 3). In the illustratedembodiment, task 500 may be associated with cargo including an objectand/or cargo including a person. In the first portion (I) of task 500,Car 1 picks up the cargo at a Location 1, an original pick-up locationassociated with task 500. Car 1 delivers the cargo to a Location 2, andcompletes the first portion (I) of task 500. The second portion (II) oftask 500 is initiated by Car 2, which picks up the cargo at Location 2and delivers the cargo to a Location 3. The second portion (II) of task500 is completed. The third portion (III) of task 500 is initiated byCar 3, which picks up the cargo at Location 3 and delivers the cargo toa Location 4, an original delivery location associated with task 500.The third portion (III) of task is completed, and task 500 is completed.

Exemplary Management Hub App

FIGS. 6 and 7 depict exemplary screen captures or “screenshots” of userinterface 216 of management hub app 212 as executed on a user computingdevice 202 (all shown in FIG. 2). The example screenshots includevarious features and functionalities of management hub app 212. Inparticular, VRA computing device 130 causes to be displayed at leastoptimal vehicle routes and vehicle analytics at user computing device202, specifically via management hub app 212.

More specifically, FIG. 6 depicts a screenshot 600 of a first page 602accessed by a user within user interface 216 of management hub app 212.First page 604 may display a menu 604 of icons 606 that may be selectedby the user to access different sections, pages, and/or functionality ofmanagement hub app 212. In the illustrated embodiment, menu 604 includesicons 606 associated with Operations (represented as a circle), BusinessTools/Reports (represented as a lined box), E-Assistance (represented asa star), Definitions (represented as a heart), and Account Settings(represented as a gear). It should be readily understood that menu 604may include additional, fewer, and/or alternative icons 606 that mayrepresent additional, fewer, and/or alternative sections, pages, and/orfunctionality within management hub app 212.

In the illustrated embodiment, the user has selected the icon 606associated with the Operations section to display first page 602. Firstpage 602 further includes a plurality of tabs 608, each tab 608associated with a different page within the Operations section ofmanagement hub app. Specifically, tabs 608 include a Status tab, a Routetab, a Schedule tab, and an Options/Preferences tab. It should bereadily understood that Operations section may include additional,fewer, and/or alternative tabs 608 that may represent additional, fewer,and/or alternative pages therein. Likewise, when any other sections areselected by the user, tabs 608 may change in accordance with the variouspages associated with each respective section.

The user has selected the Status tab to display first page 602.Accordingly, user interface 216 shows a current location of a vehicleoperating according to an optimal route generated by VRA computingdevice 130 (shown in FIG. 1). The current location is displayed as anicon 610 of the vehicle on a map 612. Also shown are a status of thevehicle, illustrated as “Active” (e.g., operating according to anoptimal route). Other statuses may include “Service” (e.g., having amaintenance/repair service performed thereon) and/or “Inactive” (e.g.,not performing tasks). First page 602 also displays a current task,illustrated as “Drop Off Passenger A,” as well as other cargo, including“Package B”.

Accordingly, the user may easily identify what cargo is currently beingtransported by the vehicle, which may include any number of person(s)and/or object(s). The status may also include a distance travelledduring operation of the current optimal route, a maintenancenotification, a maintenance status indicator (e.g., Good, Non-CriticalRepair Required, Immediate Repair Required, Offline/Disabled, etc.),and/or any other suitable information. Moreover, when a user has morethan one vehicle associated therewith (e.g., multiple associatedvehicles in one or more fleets, or multiple vehicle that operateindependently), first page 602 may further include one or more controlsthat enable the user to view the status for each respective vehicle(e.g., by selecting from a drop-down list, scrolling via back andforward arrows, etc.) and/or all vehicles at once.

Although not illustrated, it is contemplated that the Route tab, whenselected, may display the current optimal route as a list/schedule oftasks, included completed and/or upcoming tasks, their pick-up/deliverylocations, pick-up/delivery times, anticipated cargo, and the like. Itis contemplated that the Schedule tab may enable the user to schedule atask, such as requesting a ride or scheduling a packagepick-up/delivery. The Options/Preferences tab may enable the user tomodify various user-defined preferences for one or more vehicles, suchas cargo preferences (e.g., person-only, object-only, persons andobjects) and/or operation preferences, such as time(s) each vehicle isavailable, location preferences (e.g., do not travel more than X miles,restrict travel to a certain radius or within certain areas),battery/fuel preferences (e.g., operate until battery is at X % or has Xestimated miles remaining), risk settings (e.g., only allow tasks havinga risk level below a certain threshold, only allow optimal routes havingan overall risk level below a certain threshold), priority settings(e.g., prioritize objects, prioritize persons, prioritize VIPs, etc.),and the like.

FIG. 7 depicts a screenshot 700 of a second page 702 accessed by a userwithin user interface 216 of management hub app 212. In the illustratedembodiment, the user has selected the icon 606 associated with theBusiness Tools/Reports section to display second page 702. Second page702 further includes a plurality of tabs 708, each tab 708 associatedwith a different page within the Business Tools/Reports section ofmanagement hub app. Specifically, tabs 708 include a Smart Report tab,an Opportunities tab, and a Suggested Services tab. It should be readilyunderstood that Business Tools/Reports section may include additional,fewer, and/or alternative tabs 708 that may represent additional, fewer,and/or alternative pages therein.

User interface 216 displays a graph or “smart chart” 710 of one or morevehicle analytics, such as revenue generated and/or potential revenue(in the illustrated embodiment), as well as text-based reports of othervehicle analytics. Other vehicle analytics may include, for example,time in operation (e.g., in hours or a percentage of a time period suchas a day or week), lull time (e.g., the vehicle is disabled, beingserviced, or otherwise not completing tasks/generating revenue),expenses/costs, profits (e.g., revenues less costs), number of completedtasks, mileage driven, number of “social good” tasks completed, risklevel, performance ratings, and the like.

Any vehicle analytic can be displayed as an absolute value, anaverage/median, total value according to time period (e.g., “per day,”“per week,” etc.), and/or otherwise calculated and displayed, such as“revenue per hour of operation,” “profit per mile driven,” etc. Inaddition, one smart chart 710 may display one vehicle analytic or aplurality of vehicle analytics at once. Moreover, when a user has morethan one vehicle associated therewith (e.g., multiple associatedvehicles in one or more fleets, or multiple vehicle that operateindependently), second page 702 may further include one or more controlsthat enable the user to view the vehicle analytics for each respectivevehicle (e.g., by selecting from a drop-down list, scrolling via backand forward arrows, etc.) and/or all vehicles at once.

Although not illustrated, it is contemplated that, when theOpportunities tab is selected, user interface 16 may display one or morerecommendations to improve revenue generation for a vehicle.Recommendations vary depending on the specifics of each vehicle'soperation, and may generally include increasing time/locationavailability, modifying time/location availability to accommodate task“surges” (e.g., commute periods, events, etc.), varying allowed risklevels, modifying cargo preferences, adding vehicles, and the like.Selecting the Suggested Service tab may cause display of a page thatprovides information on “partner vendors,” which may encourage the userto employ the services of those vendors.

Additionally or alternatively, the Suggested Services tab may identifyparticular services the user may want and/or need to schedule for thevehicle (or, in some cases, for the user's business associated with thevehicle(s), such as marketing/advertising services), and may recommendone or more partner vendors associated with those services. In someembodiments, users may be able to employ and/or purchase those servicesdirectly through management hub app 212 (e.g., without requiringout-of-app contact with a vendor, such as via phone).

Although not illustrated, it is contemplated that, when the icon 606associated with the E-Assistance section is selected, one or moreassociated page(s) may be displayed (e.g., with or without associatedtab(s)). For example, user interface 216 may display anAccident/Incident/Recovery page selectable when a vehicle is undergoingservice, has been in an accident, is disabled, or the like. Such a pagemay enable the user to request repair/maintenance services, review thecurrent location of the vehicle and/or a current vendor servicing thevehicle, request emergency assistance, and the like. Additionally oralternatively, user interface 216 may display an Insurance page, wherethe user may review their insurance policies, request modificationthereto, request or purchase one-time insurance policies, viewrecommendations, and the like.

It is contemplated that, when the icon 606 associated with theDefinitions section is selected, one or more associated page(s) may bedisplayed (e.g., with or without associated tab(s)). For example, userinterface 216 may display a Vehicle Definition page that enables theuser to add, update, modify, and/or view one or more vehicledefinitions. User interface 216 may display a Task Definition page thatenables the user to add, update, modify, and/or view one or more taskdefinitions.

It is contemplated that, when the icon 606 associated with theDefinitions section is selected, one or more associated page(s) may bedisplayed (e.g., with or without associated tab(s)). For example, userinterface 216 may display a Profile page, where the user may register ormodify a user profile/account, such as an account type (e.g., individualuser, small business user, investor, fleet manager, etc.),personal/contact information, financial account information for depositsand/or withdrawals, insurance policy information, privacy settings, andthe like.

Exemplary User Computer Device

FIG. 8 depicts an exemplary configuration of an exemplary user computerdevice 202 that may be used with vehicle routing system 200 (shown inFIG. 2), in accordance with one embodiment of the present disclosure.User computer device 202 may be operated by a user 801 (e.g., a vehicleuser, a task source, an investor, etc.).

User computer device 202 may include a processor 802 for executinginstructions. In some embodiments, executable instructions may be storedin a memory area 804. Processor 802 may include one or more processingunits (e.g., in a multi-core configuration). Memory area 804 may be anydevice allowing information such as executable instructions and/ortransaction data to be stored and retrieved. Memory area 804 may includeone or more computer-readable media. Memory area 804 may include, but isnot limited to, random access memory (RAM) such as dynamic RAM (DRAM) orstatic RAM (SRAM), read-only memory (ROM), erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), and non-volatile RAM (NVRAM). The above memory typesare exemplary only, and are thus not limiting as to the types of memoryusable for storage of a computer program.

User computer device 202 also may include at least one media outputcomponent 806 for presenting information to user 801, such as userinterface 216 of management hub app 212 (both shown in FIG. 2) whenmanagement hub app 212 is executed on user computing device 202. Mediaoutput component 806 may be any component capable of conveyinginformation to user 801. In some embodiments, media output component 806may include an output adapter (not shown), such as a video adapterand/or an audio adapter. An output adapter may be operatively coupled toprocessor 802 and operatively couplable to an output device such as adisplay device (e.g., a liquid crystal display (LCD), light emittingdiode (LED) display, organic light emitting diode (OLED) display,cathode ray tube (CRT) display, “electronic ink” display, or a projecteddisplay) or an audio output device (e.g., a speaker or headphones).

In some embodiments, user computer device 202 may include an inputdevice 808 for receiving input from user 801. User 801 may use inputdevice 808 to, without limitation, interact with vehicle 104 and/or VRAcomputing device 130 (both shown in FIG. 1), input vehicle definitions,input task definition, review vehicle analytics, and/or review paymentinformation. Input device 808 may include, for example, a keyboard, apointing device, a mouse, a stylus, and/or a touch sensitive panel(e.g., a touch pad or a touch screen). A single component such as atouch screen may function as both an output device of media outputcomponent 806 and input device 808.

User computer device 202 may also include a communication interface 810,communicatively coupled to a remote device such as VRA computing deviceand/or vehicle 100. Communication interface 810 may include, forexample, a wired or wireless network adapter or a wireless datatransceiver for use with a mobile phone network (e.g., Global System forMobile communications (GSM), 3G, 4G or Bluetooth) or other mobile datanetwork (e.g., Worldwide Interoperability for Microwave Access (WIMAX)).

Stored in memory area 804 may be, for example, computer-readableinstructions for providing a user interface to user 801 via media outputcomponent 806 and, optionally, receiving and processing input from inputdevice 808. The user interface may include, among other possibilities, aweb browser and/or a client application. Web browsers enable users, suchas user 801, to display and interact with media and other informationtypically embedded on a web page or a website from VRA computing device130. A client application may allow user 801 to interact with, forexample, VRA computing device 130. For example, instructions may bestored by a cloud service and the output of the execution of theinstructions sent to the media output component 806.

Exemplary Computer-Implemented Method for Generating an Optimal Routefor a Vehicle that Maximizes Potential Revenue for Operation of theVehicle

FIG. 9 depicts a flow chart of an exemplary computer-implemented method900 for generating an optimal route for a vehicle (e.g., vehicle 100,shown in FIG. 1) that maximizes potential revenue for operation of thevehicle, using vehicle routing system 200 shown in FIG. 2. In theexemplary embodiment, method 900 may be performed by VRA computingdevice 130 (shown in FIG. 1).

Method 900 may include retrieving 902 a vehicle definition for thevehicle, the vehicle definition including availability parameters anddelivery preferences associated with the vehicle. In some embodiments,VRA computing device 130 is communicatively coupled to a vehicledefinition database (e.g., database 132, shown in FIG. 1) that storesvehicle definitions, wherein the vehicle definition database ispopulated by vehicle users associated with vehicles for which thevehicle definitions are stored, and wherein the vehicle definitionincludes data elements include a period of time over which the vehicleis available to complete tasks, a geographic range over which thevehicle is available to complete tasks, cargo preferences set by theassociated vehicle user, a capacity of the vehicle for cargo includingpersons, a capacity of the vehicle for cargo including objects, a makeof the vehicle, a model of the vehicle, a manufacturing year of thevehicle, an identifier of the vehicle, a vehicle type, vehicle features,a vehicle class, current vehicle location, current vehicle capacity, aperformance rating for the vehicle, and historical claim data for thevehicle.

Method 900 may also include retrieving 904, based in part on the vehicledefinition, a plurality of task definitions defining a respectiveplurality of tasks, each task including a respective cargo to bedelivered, pick-up time, delivery time, pick-up location, deliverylocation, and task value. In some embodiments, VRA computing device 130may be communicatively coupled to a task definition database (e.g.,database 132) that stores available tasks, and retrieving 904 mayfurther include: (i) identifying, based upon the vehicle definition, anavailability radius for the vehicle, the availability radius defining abounded geographical area to which the vehicle is available to traveland a period of time over which the vehicle is available to travel; (ii)generating a query including the availability radius; and/or (iii)transmitting, to the task definition database, the query including theavailability radius, wherein the query causes the task definitiondatabase to identify and retrieve the plurality of tasks that can becompleted within the availability radius of the vehicle.

Method 900 may additionally include generating 906, by executing atleast one of artificial intelligence and deep learning functionalityusing the vehicle definition and the plurality of task definitions, anoptimal route for the vehicle that includes a scheduled list of a subsetof the plurality of tasks for the vehicle to perform, wherein theoptimal route maximizes the potential revenue for operation of thevehicle within a period of time associated with the optimal route. Insome embodiments, a first task of the subset of tasks is associated witha high-value cargo, and generating 906 may include generating theoptimal route to schedule no additional tasks until completion of thefirst task. In some such embodiments, generating 906 may further includegenerating the optimal route to include a first sub-route associatedwith the first task that has a risk level at or below a risk levelthreshold associated with the first task.

Method 900 may further include transmitting 908 the optimal route to thevehicle for operation of the vehicle according to the optimal route. Insome embodiments, the vehicle may be an autonomous vehicle, and whereinthe optimal route, when received by the autonomous vehicle, may causethe autonomous vehicle to travel to pick-up and delivery locationsassociated with the subset of tasks according to the optimal route.

Method 900 may include additional, fewer, and/or alternative steps,including those described herein. For example, in some embodiments,method 900 may further include: (i) retrieving a plurality of historicalroutes travelled by a plurality of vehicles; (ii) retrieving a pluralityof completed tasks completed by the plurality of vehicles; (iii)identifying one or more patterns in the plurality of historical routesand the plurality of completed tasks; and/or (iv) updating the at leastone of the artificial intelligence and deep learning functionality basedupon the identified patterns.

In some such embodiments, method 900 may further include: (a)identifying, from the identified patterns, at least one underservedlocation that has fewer than a threshold numbers of completed tasksassociated therewith; and/or (b) generating the optimal route toprioritize tasks associated with the underserved location. In someembodiments, method 900 may further include: (a) identifying, from theidentified patterns, a lull period of time over which the plurality ofvehicles completed a lowest number of completed tasks; (b) generating aservice schedule including one or more services associated with thevehicle, a respective service time associated with each service, and arespective service vendor associated with each service, wherein eachservice time is within the lull period of time; and/or (c) transmittingthe service schedule to the vehicle for operation of the vehicleaccording to the service schedule. In some cases, the vehicle may be anautonomous vehicle, and the service schedule, when received by theautonomous vehicle, may cause the autonomous vehicle to travel tolocations associated with the respective service vendors according tothe service schedule.

In some embodiments, method 900 also includes: (i) identifying a firstevent occurring at a first location at first time, wherein the firstevent is likely to be associated with a plurality of potential tasks;and/or (ii) generating the optimal route to schedule operation of thevehicle at the first location at the first time to make the vehicleavailable to accommodate at least one potential task.

In some embodiments, the delivery preferences for the vehicle mayinclude a person-only cargo preference, and method 900 may furtherinclude: (i) filtering the plurality of tasks based upon the person-onlycargo preference to identify a plurality of person-only cargo tasks fromthe plurality of tasks, and/or (ii) generating the optimal route for thevehicle that includes the scheduled list of a subset of the plurality ofperson-only cargo tasks for the vehicle to perform. In some suchembodiments, the vehicle may include a first vehicle and the optimalroute may include a first optimal route, and method 900 may furtherinclude: (a) identifying a second vehicle operating according to asecond optimal route, wherein the second vehicle is associated with avehicle definition that does not include the person-only cargopreference and operates within an availability radius similar to anavailability radius of the first vehicle; (b) comparing a predictedrevenue associated with the second optimal route to a predicted revenueassociated with the first optimal route; (c) calculating, based upon thecomparing, a predicted loss associated with the first optimal route;and/or (d) transmitting, to a vehicle user associated with the firstvehicle, a recommendation message including the predicted loss and arecommendation that the vehicle user remove the person-only cargopreference from the vehicle definition of the first vehicle.

In some embodiments, the delivery preferences for the vehicle mayinclude an object-only cargo preference, and method 900 may furtherinclude: (i) filtering the plurality of tasks based upon the object-onlycargo preference to identify a plurality of object-only cargo tasks fromthe plurality of tasks; and/or (ii) generating the optimal route for thevehicle that includes the scheduled list of a subset of the plurality ofobject-only cargo tasks for the vehicle to perform. In some suchembodiments, the vehicle may include a first vehicle and the optimalroute may include a first optimal route, and method 900 may furtherinclude: (a) identifying a second vehicle operating according to asecond optimal route, wherein the second vehicle is associated with avehicle definition that does not include the object-only cargopreference and operates within an availability radius similar to anavailability radius of the first vehicle; (b) comparing a predictedrevenue associated with the second optimal route to a predicted revenueassociated with the first optimal route; (c) calculating, based upon thecomparing, a predicted loss associated with the first optimal route;and/or (d) transmitting, to a vehicle user associated with the firstvehicle, a recommendation message including the predicted loss and arecommendation that the vehicle user remove the object-only cargopreference from the vehicle definition of the first vehicle.

In some embodiments, the vehicle may include a first vehicle and thevehicle definition may include a first vehicle definition, the firstvehicle definition including a first availability radius for the firstvehicle that defines a bounded geographical area to which the vehicle isavailable to travel and a period of time over which the vehicle isavailable to travel. Method 900 may further include: (i) identifying,from the plurality of task definitions, a first task that extends beyondthe first availability radius of the first vehicle; (ii) retrievingvehicle definitions of other vehicles than the first vehicle thatoperate within a respective availability radius adjacent to the firstavailability radius; (iii) selecting a second vehicle from the othervehicles having a second availability radius adjacent to the firstavailability radius; (iv) generating (e.g., generating 906) the optimalroute for the first vehicle as a first optimal route that includes afirst portion of the first task, wherein the first optimal routeschedules the first vehicle to pick up cargo associated with the firsttask at the pick-up location associated with the first task and to dropoff the cargo at the second vehicle at a second location between thepick-up location and the delivery location associated with the firsttask; (v) generating a second optimal route for the second vehicle thatincludes a second portion of the first task, wherein the second optimalroute schedules the second vehicle to pick up the cargo associated withthe first task from the first vehicle at the second location and to dropoff the cargo at one of (a) the delivery location associated with thefirst task, or (b) a third vehicle at a third location between thesecond location and the delivery location associated with the firsttask; and/or (vi) transmitting (e.g., transmitting 908) the firstoptimal route to the first vehicle and the second optimal route to thesecond vehicle.

In some such embodiments, the first and second vehicles may beautonomous vehicles, and the first optimal route, when received by thefirst autonomous vehicle, may cause the first autonomous vehicle totravel to the pick-up location and the second location associated withthe first portion of the first task according to the first optimalroute. The second optimal route, when received the second autonomousvehicle, may cause the second autonomous vehicle to travel to the secondlocation and the one of (a) the delivery location, or (b) the thirdlocation associated with the second portion of the first task accordingto the second optimal route. Additionally, the first optimal route mayfurther cause the first autonomous vehicle to arrive at the secondlocation at a predetermined time, and the second optimal route may causethe second autonomous vehicle to arrive at the second location atsubstantially the same predetermined time.

Exemplary Computer-Implemented Methods for Generating an Optimal Routeand Generating Analytics Associated with Operation of the Vehicle

FIG. 10 depicts a flow chart of an exemplary computer-implemented method1000 for generating an optimal route for a vehicle (e.g., vehicle 100,shown in FIG. 1) that maximizes potential revenue for operation of thevehicle and generating analytics associated with operation of thevehicle, using vehicle routing system 200 shown in FIG. 2. In theexemplary embodiment, method 1000 may be performed by VRA computingdevice 130 (shown in FIG. 1). VRA computing device 130 may becommunicatively coupled to a plurality of vehicles, each vehicle havinga plurality of sensors (e.g., sensors 106, shown in FIG. 1) disposedthereon and configured to collect sensor data during operation of therespective vehicle.

Method 1000 may include retrieving 1002 a vehicle definition for a firstvehicle of the plurality of vehicles, the vehicle definition includingavailability parameters and delivery preferences associated with thefirst vehicle.

Method 1000 may also include retrieving 1004, based in part on thevehicle definition, a plurality of task definitions defining arespective plurality of tasks, each task including a respective cargo tobe delivered, pick-up time, delivery time, pick-up location, deliverylocation, and task value.

Method 1000 may further include generating 1006, by executing at leastone of artificial intelligence and deep learning functionality using thevehicle definition and the plurality of task definitions, an optimalroute for the vehicle that includes a scheduled list of a subset of theplurality of tasks for the vehicle to perform, wherein the optimal routemaximizes the potential revenue for operation of the vehicle within aperiod of time associated with the optimal route.

Method 1000 may additionally include transmitting 1008 the optimal routeto the first vehicle for operation of the vehicle according to theoptimal route, and receiving 1010, from the first vehicle, sensor dataduring operation of the first vehicle according to the optimal route.

Method 1000 may include additional, fewer, and/or alternative steps,including those described herein. For example, in some embodiments,method 1000 may further include determining, based upon the receivedsensor data from the first vehicle, a first risk level associated withoperation of the first vehicle according to the optimal route. In somesuch embodiments, method 1000 may further include: (i) retrieving aninsurance policy associated with the first vehicle; (ii) determiningwhether the first risk level exceeds a threshold defined in theinsurance policy; and/or (iii) when the first risk level exceeds thethreshold, transmitting a notification to a vehicle user associated withthe first vehicle that the first risk level exceeds the threshold. Insome embodiments, method 1000 may further include (iv) generating thenotification including a recommendation of a one-time supplementalinsurance policy associated with the optimal route.

In some embodiments, method 1000 may further include: (i) retrieving aninsurance policy associated with the first vehicle; (ii) determiningwhether the first risk level exceeds a threshold defined in theinsurance policy; and/or (iii) when the first risk level exceeds thethreshold, modifying the optimal route to include one or morealternative paths such that the modified optimal route has a second risklevel below the threshold.

In some embodiments, a first task of the subset of tasks may beassociated with a high-value cargo, and method 1000 may further include:(i) generating a recommendation message including a recommendation for aone-time supplemental insurance policy associated with the first task;and/or (ii) transmitting the recommendation message to a vehicle userassociated with the first vehicle.

In some embodiments, method 1000 may include generating, based upon thesensor data received from the first vehicle, a risk profile associatedwith the first vehicle. In some embodiments, method 1000 may include:(i) detecting, based upon the sensor data received from the firstvehicle, that the first vehicle requires one or more services; (ii)generating a service schedule for the first vehicle, the serviceschedule including the required one or more services, a respectiveservice time associated with each service, and a respective servicevendor associated with each service; and/or (iii) transmitting theservice schedule to the vehicle for operation of the vehicle accordingto the service schedule.

In some embodiments, the plurality of sensors disposed on the firstvehicle may include at least one sensor configured to identify check-inand check-out of cargo to and from the first vehicle, and method 1000may include monitoring the sensor data received from the first vehiclefor adherence to the optimal route. In some such embodiments, method1000 may still further include: (i) identifying, based upon themonitoring, a deviation from the optimal route; and/or (ii) transmittinga control signal to the first vehicle, the control signal operative tocause the first vehicle to at least one of: (a) generate an alarm, or(b) record at least one of audio and video at the first vehicle tocapture additional sensor data associated with the deviation.

In some embodiments, method 1000 may still further include: (i)identifying, based upon the monitoring, a deviation from the optimalroute; and/or (ii) transmitting an alert to a first responder computingdevice to report the deviation. In other embodiments, method 1000 maystill further include generating an electronic ledger of the identifiedcheck-in and check-out of the cargo to and from the first vehicle. Inother embodiments, method 1000 may still further include: (i)identifying, based upon the monitoring, a deviation from the optimalroute including an unauthorized check-in of cargo to the first vehicle;and/or (ii) transmitting a notification signal to the first vehicledirecting suspension of further operation of the first vehicle.

In some embodiments, method 1000 may include receiving sensor data fromthe plurality of vehicles. Method 1000 may further include: (i)identifying, based upon sensor data received from a subset of theplurality of vehicles having a same make and model, a need for a similarservice common to the subset of vehicles; and/or (ii) generating aservice report indicating the need for the similar service common to thesubset of vehicles for notification of a manufacturer of the subset ofvehicles. Additionally or alternatively, method 1000 may furtherinclude: (i) processing the sensor data received from the plurality ofvehicles to identify an infrastructure risk; and/or (ii) generating arisk report identifying the infrastructure risk for notification of anentity capable of responding to the infrastructure risk.

Additionally or alternatively, method 1000 may include: (i) processingthe sensor data received from the plurality of vehicles to identify anongoing infrastructure project; (ii) generating, from the sensor dataover a period of time, a time lapse of progress of the ongoinginfrastructure project; and/or (iii) generating a progress reportincluding the time lapse of progress of the ongoing infrastructureproject for notification of an entity associated with the ongoinginfrastructure project. Additionally or alternatively, method 1000 mayfurther include: (i) processing the sensor data received from theplurality of vehicles to identify a real-time emergency incident; and/or(ii) transmitting a notification to a first responder computing device,the notification including an indication of the real-time emergencyincident and a location thereof.

FIG. 11 illustrates a flow chart of another exemplarycomputer-implemented method 1100 for generating an optimal route for avehicle (e.g., vehicle 100, shown in FIG. 1) that maximizes potentialrevenue for operation of the vehicle and generating analytics associatedwith operation of the vehicle, using the vehicle routing system 200shown in FIG. 2. In the exemplary embodiment, method 11000 may beperformed by VRA computing device 130 (shown in FIG. 1).

Method 1100 may include receiving 1102, from a vehicle user associatedwith the vehicle, a vehicle definition for a first vehicle of theplurality of vehicles, the vehicle definition including availabilityparameters and delivery preferences associated with the first vehicle

Method 1100 may also include generating 1104, based in part on thevehicle definition, an optimal route for the first vehicle that includesa scheduled list of a plurality of tasks for the vehicle to perform.Each task may have an associated respective cargo to be delivered,pick-up time, delivery time, pick-up location, delivery location, andtask value. The optimal route may maximize the potential revenue foroperation of the first vehicle within a period of time associated withthe optimal route.

Method 1100 may further include transmitting 1106 the optimal route tothe first vehicle for operation of the vehicle according to the optimalroute, and receiving 1108, from the first vehicle, sensor data duringoperation of the first vehicle according to the optimal route.

Method 1100 may still further include processing 1110 the receivedsensor data to generate vehicle analytics associated with a performanceof the vehicle, generating 1112 one or more visual representations ofthe vehicle analytics, and transmitting 1114 the one or more visualrepresentations of the vehicle analytics to a computing device of thevehicle user for display within a user interface of a management hubapplication executed on the user computing device.

Method 1100 may include additional, fewer, and/or alternative steps,including those described herein. For example, in some embodiments, thevehicle may include a first vehicle, the optimal route may include afirst optimal route, and the vehicle analytics may include first vehicleanalytics. VRA computing device 130 may be communicatively coupled to aplurality of vehicles including the first vehicle, each vehicle of theplurality of vehicle having a plurality of sensors disposed thereon andconfigured to collect sensor data during operation of the respectivevehicle.

Method 1100 may further include: (i) generating second vehicle analyticsfor at least one other vehicle than the first vehicle based upon sensordata received from the at least one other vehicle during operation ofthe at least one other vehicle according to a respective other optimalroute; (ii) comparing the first vehicle analytics to the second vehicleanalytics; (iii) identifying, based upon the comparing, a differencebetween the first vehicle analytics and the second vehicle analytics;(iv) parsing the vehicle definition for the first vehicle to identify auser-defined delivery preference that accounts for the differencebetween the first vehicle analytics and the second vehicle analytics;(v) generating a recommendation that the vehicle user modify theuser-defined delivery preference; and/or (vi) transmitting therecommendation to the user computing device for display within the userinterface of the management hub application, the recommendationidentifying the difference between the first vehicle analytics and thesecond vehicle analytics, the user-defined delivery preference, and analternative user-defined delivery preference that would reduce thedifference between the first vehicle analytics and the second vehicleanalytics. In some embodiments, the user-defined delivery preference isone of a person-only cargo delivery preference and an object-only cargodelivery preference, and wherein the alternative user-defined deliverypreference is a persons-and-objects delivery preference.

In other embodiments, VRA computing device 130 may be communicativelycoupled to a plurality of service vendor computing devices associatedwith a respective plurality of service vendors that perform vehiclemaintenance and repair services. Method 1100 may further include: (i)detecting, based upon the vehicle analytics, the vehicle needs a serviceto be performed thereon; (ii) transmitting, to the vehicle, a serviceschedule including the service to be performed, a service timeassociated with the service, and a first service vendor of the pluralityof service vendors to perform the service; (iii) transmitting, to theservice vendor computing device associated with the first servicevendor, the service schedule; and/or (iv) transmitting, to the usercomputing device, an alert that the vehicle needs the service and anidentification of the first service vendor.

In some embodiments, method 1100 also includes: (i) identifying aplurality of other vehicles associated with the vehicle user; (ii)generating respective vehicle analytics for each of the plurality ofother vehicles; and/or (iii) transmitting one or more visualrepresentations of the vehicle analytics for the plurality of othervehicles to the user computing device for display within the userinterface of the management hub application.

In some embodiments, the one or more visual representations may includeone or more graphs, charts, maps, plots, and legends. In someembodiments, the vehicle analytics may include one or more of: agenerated revenue amount, percentage of the optimal route completed,path and distance travelled during operation of the vehicle according tothe optimal route, current location of the vehicle, service status,services performed on the vehicle, risk rating of the optimal route,costs of operating the vehicle during the optimal route, revenuestatistics based upon divisions of time, or revenue statistics per task.

In some embodiments, the vehicle may include a first vehicle, theoptimal route may include a first optimal route, and the vehicleanalytics may include first vehicle analytics. VRA computing device 130may be communicatively coupled to a plurality of vehicles including thefirst vehicle, each vehicle of the plurality of vehicle having aplurality of sensors disposed thereon and configured to collect sensordata during operation of the respective vehicle. Method 1100 mayinclude: (i) generating second vehicle analytics for the plurality ofvehicles based upon sensor data received from the plurality of vehiclesduring operation of the plurality of vehicles; and/or (ii) identifying,using artificial intelligence and deep learning functionality, one ormore patterns or trends of operation of the plurality of vehicles in thesecond vehicle analytics.

In some such embodiments, method 1100 may further include: (a)identifying, from the one or more identified patterns or trends, atleast one underserved location that has experienced less than athreshold amount of vehicle operation therein; (b) determining, basedupon the vehicle definition of the first vehicle, that the availabilityparameters of the first vehicle do not encompass the at least oneunderserved location; (c) generating a recommendation that the vehicleuser modify the availability parameters of the first vehicle to includethe at least one underserved location, the recommendation identifyingthe at least one underserved location and the availability parameters ofthe first vehicle; and/or (d) transmitting the recommendation to theuser computing device for display within the user interface of themanagement hub application.

Exemplary Embodiments & Functionality

In one exemplary aspect, a vehicle routing and analytics (VRA) computingdevice for generating an optimal route for a vehicle that maximizespotential revenue for operation of the vehicle may be provided. The VRAcomputing device may include at least one processor in communicationwith a memory. The at least one processor may be programmed to: (i)receive or retrieve a vehicle definition for the vehicle, the vehicledefinition including availability parameters and delivery preferencesassociated with the vehicle; (ii) receive or retrieve, based in part onthe vehicle definition, a plurality of task definitions defining arespective plurality of tasks, each task including a respective cargo tobe delivered, pick-up time, delivery time, pick-up location, deliverylocation, and task value; (iii) generate, by executing at least one ofartificial intelligence and deep learning (or machine learning)functionality using the vehicle definition and the plurality of taskdefinitions, an optimal route for the vehicle that includes a scheduledlist of a subset of the plurality of tasks for the vehicle to perform,wherein the optimal route maximizes the potential revenue for operationof the vehicle within a period of time associated with the optimalroute; and/or (iv) transmit the optimal route to the vehicle foroperation of the vehicle according to the optimal route.

One enhancement may be, where the vehicle is an autonomous vehicle, thatthe optimal route, when received by the autonomous vehicle, causes theautonomous vehicle to travel to pick-up and delivery locationsassociated with the subset of tasks according to the optimal route.

Another enhancement may be, where the VRA computing device iscommunicatively coupled to a task definition database that storesavailable tasks, that to retrieve the plurality of task definitions, theat least one processor is further programmed to: (i) identify, basedupon the vehicle definition, an availability radius for the vehicle, theavailability radius defining a bounded geographical area to which thevehicle is available to travel and a period of time over which thevehicle is available to travel; (ii) generate a query including theavailability radius; and/or (iii) transmit, to the task definitiondatabase, the query including the availability radius, wherein the querycauses the task definition database to identify and retrieve theplurality of tasks that can be completed within the availability radiusof the vehicle.

Another enhancement may be, where the VRA computing device iscommunicatively coupled to a vehicle definition database that storesvehicle definitions, the vehicle definition database populated byvehicle users associated with vehicles for which the vehicle definitionsare stored, that the vehicle definition includes data elements include aperiod of time over which the vehicle is available to complete tasks, ageographic range over which the vehicle is available to complete tasks,cargo preferences set by the associated vehicle user, a capacity of thevehicle for cargo including persons, a capacity of the vehicle for cargoincluding objects, a make of the vehicle, a model of the vehicle, amanufacturing year of the vehicle, an identifier of the vehicle, avehicle type, vehicle features, a vehicle class, current vehiclelocation, current vehicle capacity, a performance rating for thevehicle, and historical claim data for the vehicle.

A further enhancement may be, where a first task of the subset of tasksis associated with a high-value cargo, the at least one processor isfurther programmed to generate the optimal route to schedule noadditional tasks until completion of the first task. The at least oneprocessor may be further programmed to generate the optimal route toinclude a first sub-route associated with the first task that has a risklevel at or below a risk level threshold associated with the first task.

In some cases, the at least one processor may be further programmed to:(i) retrieve a plurality of historical routes travelled by a pluralityof vehicles; (ii) retrieve a plurality of completed tasks completed bythe plurality of vehicles; (iii) identify one or more patterns in theplurality of historical routes and the plurality of completed tasks;and/or (iv) update the at least one of the artificial intelligence anddeep learning functionality based upon the identified patterns. The atleast one processor may be further programmed to: (a) identify, from theidentified patterns, at least one underserved location that has fewerthan a threshold numbers of completed tasks associated therewith; and/or(b) generate the optimal route to prioritize tasks associated with theunderserved location.

The at least one processor may be further configured to: (a) identify,from the identified patterns, a lull period of time over which theplurality of vehicles completed a lowest number of completed tasks; (b)generate a service schedule including one or more services associatedwith the vehicle, a respective service time associated with eachservice, and a respective service vendor associated with each service,wherein each service time is within the lull period of time; and/or (c)transmit the service schedule to the vehicle for operation of thevehicle according to the service schedule. In some instances, where thevehicle is an autonomous vehicle, the service schedule, when received bythe autonomous vehicle, may cause the autonomous vehicle to travel tolocations associated with the respective service vendors according tothe service schedule.

In other enhancements, the at least one processor may be furtherprogrammed to: (i) identify a first event occurring at a first locationat first time, wherein the first event is likely to be associated with aplurality of potential tasks; and/or (ii) generate the optimal route toschedule operation of the vehicle at the first location at the firsttime to make the vehicle available to accommodate at least one potentialtask.

Additionally or alternatively, the delivery preferences for the vehicleinclude a person-only cargo preference, and the at least one processormay be further programmed to: (i) filter the plurality of tasks basedupon the person-only cargo preference to identify a plurality ofperson-only cargo tasks from the plurality of tasks; and/or (ii)generate the optimal route for the vehicle that includes the scheduledlist of a subset of the plurality of person-only cargo tasks for thevehicle to perform. In some cases, where the vehicle includes a firstvehicle and the optimal route includes a first optimal route, and the atleast one processor may be further programmed to: (a) identify a secondvehicle operating according to a second optimal route, wherein thesecond vehicle is associated with a vehicle definition that does notinclude the person-only cargo preference and operates within anavailability radius similar to an availability radius of the firstvehicle; (b) compare a predicted revenue associated with the secondoptimal route to a predicted revenue associated with the first optimalroute; (c) calculate, based upon the comparing, a predicted lossassociated with the first optimal route; and/or (d) transmit, to avehicle user associated with the first vehicle, a recommendation messageincluding the predicted loss and a recommendation that the vehicle userremove the person-only cargo preference from the vehicle definition ofthe first vehicle.

Additionally or alternatively, the delivery preferences for the vehicleinclude an object-only cargo preference, and the at least one processormay be further programmed to: (i) filter the plurality of tasks basedupon the object-only cargo preference to identify a plurality ofobject-only cargo tasks from the plurality of tasks; and/or (ii)generate the optimal route for the vehicle that includes the scheduledlist of a subset of the plurality of object-only cargo tasks for thevehicle to perform. In some cases, where the vehicle includes a firstvehicle and the optimal route includes a first optimal route, and the atleast one processor may be further programmed to: (a) identify a secondvehicle operating according to a second optimal route, wherein thesecond vehicle is associated with a vehicle definition that does notinclude the object-only cargo preference and operates within anavailability radius similar to an availability radius of the firstvehicle; (b) compare a predicted revenue associated with the secondoptimal route to a predicted revenue associated with the first optimalroute; (c) calculate, based upon the comparing, a predicted lossassociated with the first optimal route; and/or (d) transmit, to avehicle user associated with the first vehicle, a recommendation messageincluding the predicted loss and a recommendation that the vehicle userremove the object-only cargo preference from the vehicle definition ofthe first vehicle.

Another enhancement may be, where the vehicle includes a first vehicleand the vehicle definition includes a first vehicle definition, thefirst vehicle definition including a first availability radius for thefirst vehicle that defines a bounded geographical area to which thevehicle is available to travel and a period of time over which thevehicle is available to travel, the at least one processor is furtherprogrammed to: (i) identify, from the plurality of task definitions, afirst task that extends beyond the first availability radius of thefirst vehicle; (ii) retrieve vehicle definitions of other vehicles thanthe first vehicle that operate within a respective availability radiusadjacent to the first availability radius; (iii) select a second vehiclefrom the other vehicles having a second availability radius adjacent tothe first availability radius; (iv) generate the optimal route for thefirst vehicle as a first optimal route that includes a first portion ofthe first task, wherein the first optimal route schedules the firstvehicle to pick up cargo associated with the first task at the pick-uplocation associated with the first task and to drop off the cargo at thesecond vehicle at a second location between the pick-up location and thedelivery location associated with the first task; (v) generate a secondoptimal route for the second vehicle that includes a second portion ofthe first task, wherein the second optimal route schedules the secondvehicle to pick up the cargo associated with the first task from thefirst vehicle at the second location and to drop off the cargo at one of(a) the delivery location associated with the first task, or (b) a thirdvehicle at a third location between the second location and the deliverylocation associated with the first task; and/or (vi) transmit the firstoptimal route to the first vehicle and the second optimal route to thesecond vehicle.

In some instances, wherein the first and second vehicles are autonomousvehicles, the first optimal route, when received by the first autonomousvehicle, may cause the first autonomous vehicle to travel to the pick-uplocation and the second location associated with the first portion ofthe first task according to the first optimal route, and the secondoptimal route, when received the second autonomous vehicle, may causethe second autonomous vehicle to travel to the second location and theone of (i) the delivery location, or (ii) the third location associatedwith the second portion of the first task according to the secondoptimal route. In some cases, first optimal route may further cause thefirst autonomous vehicle to arrive at the second location at apredetermined time, and the second optimal route may cause the secondautonomous vehicle to arrive at the second location at substantially thesame predetermined time.

It should be readily understood that the VRA computing device mayimplement a method including steps similar to any of the foregoingfunctionality described herein. In addition, it should be readilyunderstood that any of the foregoing functionality may be implemented ascomputer-executable instructions stored on at least one non-transitorycomputer-readable storage medium.

In another exemplary aspect, a vehicle routing and analytics (VRA)computing device for generating an optimal route for a vehicle to travelthat maximizes potential revenue for operation of the vehicle may beprovided. The VRA computing device may be communicatively coupled to aplurality of vehicles, each vehicle having a plurality of sensorsdisposed thereon and configured to collect sensor data during operationof the respective vehicle. The VRA computing device may include at leastone processor in communication with a memory. The at least one processormay be programmed to: (i) retrieve a vehicle definition for a firstvehicle of the plurality of vehicles, the vehicle definition includingavailability parameters and delivery preferences associated with thefirst vehicle; (ii) retrieve, based in part on the vehicle definition, aplurality of task definitions defining a respective plurality of tasks,each task including a respective cargo to be delivered, pick-up time,delivery time, pick-up location, delivery location, and task value;(iii) generate, by executing at least one of artificial intelligence anddeep learning functionality using the vehicle definition and theplurality of task definitions, an optimal route for the vehicle thatincludes a scheduled list of a subset of the plurality of tasks for thevehicle to perform, wherein the optimal route maximizes the potentialrevenue for operation of the vehicle within a period of time associatedwith the optimal route; (iv) transmit the optimal route to the firstvehicle for operation of the vehicle according to the optimal route;and/or (v) receive, from the first vehicle, sensor data during operationof the first vehicle according to the optimal route.

One enhancement may be that the at least one processor is furtherprogrammed to determine, based upon the received sensor data from thefirst vehicle, a first risk level associated with operation of the firstvehicle according to the optimal route. In some cases, the at least oneprocessor may be further programmed to: (i) retrieve an insurance policyassociated with the first vehicle; (ii) determine whether the first risklevel exceeds a threshold defined in the insurance policy; and/or (iii)when the first risk level exceeds the threshold, transmit a notificationto a vehicle user associated with the first vehicle that the first risklevel exceeds the threshold. In some instances, the at least oneprocessor may be further programmed to generate the notificationincluding a recommendation of a one-time supplemental insurance policyassociated with the optimal route. In other cases, the at least oneprocessor may be further programmed to: (i) retrieve an insurance policyassociated with the first vehicle; (ii) determine whether the first risklevel exceeds a threshold defined in the insurance policy; and/or (iii)when the first risk level exceeds the threshold, modify the optimalroute to include one or more alternative paths such that the modifiedoptimal route has a second risk level below the threshold.

In some cases, where a first task of the subset of tasks is associatedwith a high-value cargo, the at least one processor may be furtherprogrammed to: (i) generate a recommendation message including arecommendation for a one-time supplemental insurance policy associatedwith the first task, such as a usage-based insurance (UBI) policy havinga premium based upon the first task and/or an associated route orportion of the route; and/or (ii) transmit the recommendation message(or a quote for the UBI policy) to a vehicle user associated with thefirst vehicle and/or to the first vehicle.

Another enhancement may be where the at least one processor is furtherprogrammed to generate, based upon the sensor data received from thefirst vehicle, a risk profile associated with the first vehicle.

In some instances, the at least one processor may be further programmedto: (i) detect, based upon the sensor data received from the firstvehicle, that the first vehicle requires one or more services; (ii)generate a service schedule for the first vehicle, the service scheduleincluding the required one or more services, a respective service timeassociated with each service, and a respective service vendor associatedwith each service; and/or (iii) transmit the service schedule to thevehicle for operation of the vehicle according to the service schedule.

One enhancement may be, where the plurality of sensors disposed on thefirst vehicle include at least one sensor configured to identifycheck-in and check-out of cargo to and from the first vehicle, that theat least one processor is further programmed to monitor the sensor datareceived from the first vehicle for adherence to the optimal route. Insome cases, the at least one processor may be further programmed to: (i)identify, based upon the monitoring, a deviation from the optimal route;and/or (ii) transmit a control signal to the first vehicle, the controlsignal operative to cause the first vehicle to at least one of: (a)generate an alarm, or (b) record at least one of audio and video at thefirst vehicle to capture additional sensor data associated with thedeviation. In other cases, the at least one processor may be furtherprogrammed to: (i) identify, based upon the monitoring, a deviation fromthe optimal route; and/or (ii) transmit an alert to a first respondercomputing device to report the deviation.

Additionally or alternatively, the at least one processor may be furtherprogrammed to generate an electronic ledger of the identified check-inand check-out of the cargo to and from the first vehicle. Additionallyor alternatively, the at least one processor may be further programmedto: (i) identify, based upon the monitoring, a deviation from theoptimal route including an unauthorized check-in of cargo to the firstvehicle; and/or (ii) transmit a notification signal to the first vehicledirecting suspension of further operation of the first vehicle.

In some aspects, the at least one processor may be further programmed toreceive sensor data from the plurality of vehicles. The at least oneprocessor may be further programmed to: (i) identify, based upon sensordata received from a subset of the plurality of vehicles having a samemake and model, a need for a similar service common to the subset ofvehicles; and/or (ii) generate a service report indicating the need forthe similar service common to the subset of vehicles for notification ofa manufacturer of the subset of vehicles. Additionally or alternatively,the at least one processor may be further programmed to: (i) process thesensor data received from the plurality of vehicles to identify aninfrastructure risk; and/or (ii) generate a risk report identifying theinfrastructure risk for notification of an entity capable of respondingto the infrastructure risk.

In some cases, the at least one processor is further programmed to: (i)process the sensor data received from the plurality of vehicles toidentify an ongoing infrastructure project; (ii) generate, from thesensor data over a period of time, a time lapse of progress of theongoing infrastructure project; and/or (iii) generate a progress reportincluding the time lapse of progress of the ongoing infrastructureproject for notification of an entity associated with the ongoinginfrastructure project. Additionally or alternatively, the at least oneprocessor may be further programmed to: (i) process the sensor datareceived from the plurality of vehicles to identify a real-timeemergency incident; and/or (ii) transmit a notification to a firstresponder computing device, the notification including an indication ofthe real-time emergency incident and a location thereof.

It should be readily understood that the VRA computing device mayimplement a method including steps similar to any of the foregoingfunctionality described herein. In addition, it should be readilyunderstood that any of the foregoing functionality may be implemented ascomputer-executable instructions stored on at least one non-transitorycomputer-readable storage medium.

In yet another exemplary aspect, a vehicle routing and analytics (VRA)computing device for generating an optimal route for a vehicle to travelthat maximizes potential revenue for operation of the vehicle andgenerating analytics associated with operation of the vehicle may beprovided. The VRA computing device may be communicatively coupled to thevehicle, the vehicle having a plurality of sensors disposed thereon andconfigured to collect sensor data during operation thereof. The VRAcomputing device may include at least one processor in communicationwith a memory, wherein the at least one processor may be programmed to:(i) receive, from a vehicle user associated with the vehicle, a vehicledefinition for a first vehicle of the plurality of vehicles, the vehicledefinition including availability parameters and delivery preferencesassociated with the first vehicle; (ii) generate, based in part on thevehicle definition, an optimal route for the first vehicle that includesa scheduled list of a plurality of tasks for the vehicle to perform,each task having an associated respective cargo to be delivered, pick-uptime, delivery time, pick-up location, delivery location, and taskvalue, wherein the optimal route maximizes the potential revenue foroperation of the first vehicle within a period of time associated withthe optimal route; (iii) transmit the optimal route to the first vehiclefor operation of the vehicle according to the optimal route; (iv)receive, from the first vehicle, sensor data during operation of thefirst vehicle according to the optimal route; (v) process the receivedsensor data to generate vehicle analytics associated with a performanceof the first vehicle; (vi) generate one or more visual representationsof the vehicle analytics; and/or (vii) transmit the one or more visualrepresentations of the vehicle analytics to a computing device of thevehicle user for display within a user interface of a management hubapplication executed on the user computing device.

One enhancement may be where the one or more visual representationsinclude one or more graphs, charts, maps, plots, and legends. Anotherenhancement may be where the vehicle analytics include one or more of: agenerated revenue amount, percentage of the optimal route completed,path and distance travelled during operation of the vehicle according tothe optimal route, current location of the vehicle, service status,services performed on the vehicle, risk rating of the optimal route,costs of operating the vehicle during the optimal route, revenuestatistics based upon divisions of time, or revenue statistics per task.

In some cases, where the vehicle includes a first vehicle, the optimalroute includes a first optimal route, and the vehicle analytics includefirst vehicle analytics, the VRA computing device may be communicativelycoupled to a plurality of vehicles including the first vehicle, eachvehicle of the plurality of vehicle having a plurality of sensorsdisposed thereon and configured to collect sensor data during operationof the respective vehicle. The at least one processor may be furtherprogrammed to: (i) generate second vehicle analytics for at least oneother vehicle than the first vehicle based upon sensor data receivedfrom the at least one other vehicle during operation of the at least oneother vehicle according to a respective other optimal route; (ii)compare the first vehicle analytics to the second vehicle analytics;(iii) identify, based upon the comparing, a difference between the firstvehicle analytics and the second vehicle analytics; (iv) parse thevehicle definition for the first vehicle to identify a user-defineddelivery preference that accounts for the difference between the firstvehicle analytics and the second vehicle analytics; (v) generate arecommendation that the vehicle user modify the user-defined deliverypreference; and/or (vi) transmit the recommendation to the usercomputing device for display within the user interface of the managementhub application, the recommendation identifying the difference betweenthe first vehicle analytics and the second vehicle analytics, theuser-defined delivery preference, and an alternative user-defineddelivery preference that would reduce the difference between the firstvehicle analytics and the second vehicle analytics. In some instances,the user-defined delivery preference is one of a person-only cargodelivery preference and an object-only cargo delivery preference, andthe alternative user-defined delivery preference is apersons-and-objects delivery preference.

Another enhancement may be, where the VRA computing device is furthercommunicatively coupled to a plurality of service vendor computingdevices associated with a respective plurality of service vendors thatperform vehicle maintenance and repair services, that the at least oneprocessor is further programmed to: (i) detect, based upon the vehicleanalytics, the vehicle needs a service to be performed thereon; (ii)transmit, to the vehicle, a service schedule including the service to beperformed, a service time associated with the service, and a firstservice vendor of the plurality of service vendors to perform theservice; (iii) transmit, to the service vendor computing deviceassociated with the first service vendor, the service schedule; and/or(iv) transmit, to the user computing device, an alert that the vehicleneeds the service and an identification of the first service vendor.

In some instances, the at least one processor may be further programmedto: (i) identify a plurality of other vehicles associated with thevehicle user; (ii) generate respective vehicle analytics for each of theplurality of other vehicles; and/or (iii) transmit one or more visualrepresentations of the vehicle analytics for the plurality of othervehicles to the user computing device for display within the userinterface of the management hub application.

Another enhancement may be, where the vehicle includes a first vehicle,the optimal route includes a first optimal route, the vehicle analyticsinclude first vehicle analytics, the VRA computing device iscommunicatively coupled to a plurality of vehicles including the firstvehicle, each vehicle of the plurality of vehicle having a plurality ofsensors disposed thereon and configured to collect sensor data duringoperation of the respective vehicle, that the at least one processor isfurther programmed to: (i) generate second vehicle analytics for theplurality of vehicles based upon sensor data received from the pluralityof vehicles during operation of the plurality of vehicles; and/or (ii)identify, using artificial intelligence and deep learning functionality,one or more patterns or trends of operation of the plurality of vehiclesin the second vehicle analytics.

In some cases, the at least one processor may be further programmed to:(a) identify, from the one or more identified patterns or trends, atleast one underserved location that has experienced less than athreshold amount of vehicle operation therein; (b) determine, based uponthe vehicle definition of the first vehicle, that the availabilityparameters of the first vehicle do not encompass the at least oneunderserved location; (c) generate a recommendation that the vehicleuser modify the availability parameters of the first vehicle to includethe at least one underserved location, the recommendation identifyingthe at least one underserved location and the availability parameters ofthe first vehicle; and/or (d) transmit the recommendation to the usercomputing device for display within the user interface of the managementhub application.

It should be readily understood that the VRA computing device mayimplement a method including steps similar to any of the foregoingfunctionality described herein. In addition, it should be readilyunderstood that any of the foregoing functionality may be implemented ascomputer-executable instructions stored on at least one non-transitorycomputer-readable storage medium.

In a further exemplary aspect, a computer-implemented method ofdirecting an autonomously floating delivery autonomous vehicle may beprovided. The method may include: (i) receiving, via one or moreprocessors, servers, and/or transceivers, autonomous vehicle (AV)condition data from multiple autonomous vehicles and other sources (suchas smart infrastructure or intelligent homes) via wireless communicationand/or data transmission over one or more radio frequency links, thecondition data being generated by autonomous vehicle-mounted sensors andindicating weather, road, traffic, congestion, or accident conditions;(ii) retrieving from a memory unit or receiving via wirelesscommunication or data transmission over one or more radio links, via oneor more processors, servers, and/or transceivers, multiple servicerequests generated by multiple customer computing devices, each servicerequest including a pick-up and drop-off address, location, orcoordinates, each service request including information identifying oneor more passengers or type and weight of one or more packages; (iii)calculating, via one or more processors or servers, an overall routethat the floating delivery autonomous vehicle will travel from anorigination point to a final destination, the overall route includingeach pick-up and drop-off point of the passengers and packages (asidentified in the service requests) as a lowest cost waypoint along orwithin the overall route, the waypoints being calculated as being lowestcost based upon AV condition data and/or passenger or package weight orsize information; (iv) routing or directing, via one or more processorsor servers, the delivery autonomous vehicle along the overall route topick-up and drop-off passengers and packages at each intermediatewaypoint along the overall route; (v) receiving, via one or moreprocessors or servers, a new or additional electronic service requestfor pick-up and delivery of an additional passenger and/or package, theadditional electronic service request including pick-up and drop-offpoint or location information; (vi) continuing, via one or moreprocessors, servers, and/or transceivers, to receive updated AVcondition data via wireless communication or data transmission over oneor more radio links, the condition data including traffic, weather,congestion, road, and/or accident data, the condition data may begenerated by autonomous vehicles or other sources, such as smartinfrastructure, intelligent homes, or mobile devices; (vii) while thedelivery AV is floating along the overall route, dynamically updatingthe overall route, via one or more processors and/or servers, bycalculating the lowest cost route that includes the intermediate pick-upand drop-off locations or points in the additional service request(s) asadditional waypoints along the overall route, the lowest cost updateroute may be calculated based upon the update AV condition data or otherdata received since the overall route was initially calculated; (viii)directing, via one or more processors and/or servers, the delivery AValong the dynamically updated overall route; and/or (ix) updating, viaone or more processors and/or servers, a status hub or user interfacedisplaying a current status and/or location of each passenger and/orpackage.

In one enhancement, the AV condition data may be generated or collectedby autonomous vehicle-mounted sensors, and may include telematics(including speed, GPS location, route, direction, cornering, braking,and acceleration data) and image data. In another enhancement, the AVcondition data may be generated or collected by smart infrastructure,mobile devices, intelligent homes, drones, planes, and/or satellites.

In some instances, the lowest cost overall route may be calculated basedupon shortest time or shortest distance traveled to complete to overallroute. In some instances, the lowest cost overall route may becalculated based upon optimizing downtime of the delivery autonomousvehicle for maintenance purposes. In further instances, the lowest costoverall route may be calculated based upon optimizing downtime of thedelivery autonomous vehicle for refueling or recharging purposes. Inother instances, the lowest cost overall route may be calculated basedupon optimizing safety of traveling the overall route. In someinstances, the lowest cost overall route may be calculated based uponweather along the route, and dropping and picking up packages orpassengers in clear or sunny weather. In still further instances, thelowest cost overall route may be calculated based upon the leasttraffic, congestion, or road construction along the route.

It should be readily understood that any of the above-described stepsmay be implemented by a computer system for remotely and/or locallydirecting an autonomously floating delivery autonomous vehicle, whichmay include one or more local or remote processors, sensors,transceivers, and/or servers configured to implement any of theabove-described steps. In addition, any of the above-described steps maybe stored and executed as computer-executable instructions on at leastone non-transitory computer-readable storage medium.

In another exemplary aspect, a computer system for directing a floatingdelivery autonomous vehicle may be provided. The computer system mayinclude at least one of: one or more processors, one or more sensors,one or more transceivers, and one or more servers. The computer systemmay be configured to: (i) receive autonomous vehicle (AV) condition datafrom at least one source including a plurality of AVs, the AV conditiondata generated by AV-mounted sensors and indicating weather, road,traffic, congestion, or accident conditions; (ii) retrieve, from amemory unit, a plurality of service requests generated by acorresponding plurality of customer computing devices, each servicerequest including a pick-up location, a drop-off location, andinformation identifying (i) one or more passengers, or (ii) a type and aweight of one or more packages; (iii) calculate an overall route thatthe floating delivery AV will travel from an origination location to afinal location, the overall route including a respective pick-up anddrop-off location identified in a subset of the plurality of servicerequests, including calculating the overall route as a lowest cost routebetween all of the respective pick-up and drop-off locations aswaypoints, based upon at least one of: (a) the AV condition data, and(b) passenger or package information identified in the subset of servicerequests; (iv) direct the delivery AV to travel the overall route toeach pick-up and drop-off location and to pick-up and drop off the (a)one or more passengers or (b) one or more packages at each respectivepick-up and drop-off location as indicated in the subset of theplurality of service requests; (v) receive an additional service requestfor pick-up and drop-off of an additional (a) one or more passengers or(b) one or more packages, the additional service request including acorresponding pick-up and drop-off locations; (vi) continuously receiveupdated AV condition data; (vii) while directing the delivery AV alongthe overall route, dynamically update the overall route by calculatingan updated lowest cost route that includes the pick-up and drop-offlocations in the additional service request as additional waypointsalong the overall route, based upon at least one of (a) the updated AVcondition data or (b) other data received since the overall route wasinitially calculated; (viii) direct the delivery AV along thedynamically updated overall route; and/or (ix) update a remote statushub with location information associated with the delivery AV, thestatus hub configured to display at least one of a current status andcurrent location of each (a) one or more passengers or (b) one or morepackages transported by the delivery AV.

In one enhancement, the AV condition data may include telematics dataincluding speed, GPS location, route, direction, cornering, braking, andacceleration data of the respective AV vehicles and image data.

In some instances, the AV condition data may be generated at at leastone of: smart infrastructure, mobile devices, intelligent homes, drones,planes, or satellites.

In one enhancement, the lowest cost overall route may be calculatedbased upon shortest time or shortest distance traveled to complete tooverall route. In another enhancement, the lowest cost overall route maybe calculated based upon optimizing downtime of the delivery AV for atleast one of maintenance, refueling, or recharging. In a furtherenhancement, the lowest cost overall route may be calculated based uponoptimizing safety of traveling the overall route. In anotherenhancement, the lowest cost overall route may be calculated based uponweather along the route, including prioritizing picking up and droppingoff the at least one of (i) one or more passengers and (ii) one or morepackages in clear weather. In one enhancement, the lowest cost overallroute may be calculated based upon a route including the least traffic,congestion, or road construction along the route.

It should be readily understood that the computing system may implementa method including steps similar to any of the foregoing functionalitydescribed herein. In addition, it should be readily understood that anyof the foregoing functionality may be implemented as computer-executableinstructions stored on at least one non-transitory computer-readablestorage medium.

Additional Functionality

As autonomous vehicles become mainstream, corporations, manufacturers,and private owners will explore how to receive the most value,efficiency, and revenue from each autonomous vehicle (vehicles mayinclude: Car, Flying Car, Truck, Van, Cargo Vehicle, Drone, Plane, Semi,Construction Class and Farm, Recreation, Multi-Cargo Class, andUnknown).

In exploring best autonomous vehicle use, value, and profit, audienceswill ultimately move to a system of: (a) autonomously delivering peopleand multiple classes of cargo: packages, perishables, materials, andmail; (b) delivering those items locally, regionally, and domestically;(c) providing multiple types of delivery services, in the same vehicleat the same or different times, 24/7/365; and/or (d) floating seamlesslyfrom opportunity to opportunity between multiple classes of cargo toachieve the highest value and profit

The systems and methods described herein may provide services, support,and alignments, which will enable corporations, manufacturers, andprivate owners of autonomous vehicles, separately, to: (i) maximizerevenue opportunities; (ii) capture, automatically synthesize, and storedata to create ultra-efficient activity, routing, and logisticsstrategies; (iii) capture infrastructure data points for use inunderstanding risk or issue, reporting to local governance; (iv) capturevehicle metrics data point for reporting vehicle issues and performance;(v) create a continuous, adaptive system for right-sizing the rightcargo for the right vehicle and the right complementary route at theright time; (vi) create a hub system of support, maintenance, andservices to keep vehicles operating and creating revenue 24/7/365; (vii)create ultra-efficient methods to validate booking, pickup, tracking,delivering passengers, inventory, cargo, mail; (viii) understandoverall, live, real-time risks to be able to assess insurance andprotection needs; (ix) provide safety and security to passengers, cargo,and mail; and/or (x) create separate management and support hubs as aservice for corporations and private owners that encompasses businessand ownership ecosystems including: (a) data capture, analysis, andstorage (AI, blockchain, AWS, deep learning, data security as aservice); (b) financial services: vehicle financing & leasing products,fleet financing, banking, crypto/digital currency; (c) small businessservices: metrics & opportunities analysis, accounting, insurance,investments, benefits; (d) insurance, claims, and accident/incidentrecovery services; (e) maintenance and support services; (f) legalservices; (g) advertising, marketing, app, ratings and experiencesservices; (h) guest services; (i) cargo and passenger booking, tracking,and delivery validation services; and/or (j) subscription and investmentservices.

The systems and methods described herein may enable capturing market,consumer, and vehicle data, automatically synthesizing that data, andcreating ultra-efficient activity, routing, and logistics strategies,referred to generally as “autonomous float strategy and strategyrecommendations.” The vehicle routing system may be configured to: (i)utilize Al, cloud data storage, and deep learning to understandreal-time local, regional, and domestic market and consumer use patternsfor ride share, package pickup and delivery, and other cargo pickup anddelivery; (ii) utilize Al and deep learning to understand andautomatically employ strategies for best class or closest vehicle toimplement, best location (local, regional, domestic, or all), maxbusiness and revenue opportunity, best competition space or locationunderserved space; (iii) utilize Al and deep learning to gatherinformation from smart things calendars, user calendar and social mediaif enabled, publicly available social media, and news sources to gainunderstanding of real-time events and area activities and employstrategies to service those areas; (iv) utilize Al and deep learning tounderstand patterns of businesses that have a large quantity ofdelivery/return package traffic and locations delivered from/returned toenable service to those providers; (v) utilize AI and deep learning tounderstand and employ strategies for when a ride share,package/cargo/mail pickup/delivery, or a combination of people and cargois appropriate for max revenue potential; (vi) utilize AI and deeplearning to understand and employ strategies for when maximum safety andsecurity is needed, such as the transport of a child, senior, disabled,or a high-value package; (vii) utilize AI and deep learning patterns toemploy strategies for best down-times for fueling, maintenance, andrepair (which could be while en route to picking up cargo or people ordelivering cargo); (viii) unable an app to show one or more vehicles'current strategy, performance, location, current use, revenue generated,maintenance/repair, vehicle issues, and AI-generated recommendations forfuture use if not using an autonomous Float strategy; and/or (ix) enableowners/subscribers/investors to receive smart reports and be able toadjust use and maintenance strategies as desired.

The systems and methods described herein may also enable utilizeon-board cameras, sensors, and AI to capture vehicle data,infrastructure, environmental elements, road way, business, andneighborhood data points, in order to understand overall, live,real-time risks to be able to assess insurance, protection, and solutionneeds. The vehicle routing system may additionally (i) utilize data tounderstand real-time risks and liabilities to vehicle(s), people,interstate, street, roadway technology and roadway lights, buildings,houses, and landscape; (ii) create automatic reports for localgovernance highlighting infrastructure issues, changes, technology andconstruction progress and upgrade, work needing to be performed ormaintained, and crime and offer solutions, strategies, and serviceswhich reduce risks, liabilities, and costs and potentially reducinginsurance claims; (iii) create automatic reports highlighting businessand residential consumer risks and liabilities and offering solutionsand services to reduce risks and potentially lower insurance costs andraise property value or revenue potential; (iv) create automatic reportsregarding real-time ambient environment including traffic, weather,disaster, and other elements which may elevate risk owner/user risk orclaims activity; (v) capture vehicle metric data points for reportingvehicle issues and performance back to manufacturer; and/or (vi) usethese data points to create pattern and risk profiles for each vehicleand its use, each area traveled in or through, each passenger, andpotentially each vehicle owner.

In some embodiments, the vehicle routing system may enable the passivelistening and passive data capture of video, audio, and GPS andreporting to proper enforcement, which could help increase success ofresponding to emergencies, accidents, disasters or finding missingpersons (amber alerts), persons of interest (wanted criminals), crimesin progress, fires, cries for help and provide data evidence within anarea of travel if a crime or incident has been committed. Additionallyor alternatively, the pattern and risk profiles could then be used tocreate insurance models—such as the creation of an on-demand insuranceproduct based upon passenger and or packages in the vehicle—that arecustomized to each vehicle, passenger, and cargo provider

The present disclosure provides for creating a continuous, adaptivesystem for right-sizing the right cargo for the right vehicle and theright complementary route at the right time, by utilizing vehiclesensors and AI to create in-vehicle cargo storage/people/mail placementstrategies enabling realization of max cargo load, optimumpickup/offload speed, and highest daily revenue. The vehicle routingsystem may additionally or alternatively utilize Al, model training,and/or adaptive learning to create a pickup/delivery routing strategybased upon the following factors: time; vehicle model and type; currentvehicle location; current cargo; dimensions of individualpackage/people; weight of individual package/people elements and totalcargo; purpose of transportation; cargo or people to pickup/deliver;current location and future destination of person/package/cargo: startpoint, end point, waypoints, transfer point(s); weather; current areaconstruction and road closures; active emergency route status; timerequested for delivery if package/cargo; passenger subscription status(future packages tiers may enable choice of rate class, such as privatedirect routing or routing with package delivery on route todestination); delivery of person/people, if enabled due to routedirection agreement, being first priority; human emergency status;environmental emergency status; area and route risk data; area events;rider or cargo provider rating; legal data, such as protective orderslimiting distance; route continuation (enablement of rider or cargo tochange vehicles to be delivered at fastest pace); routing to completeroute continuation strategy; in-route rejection of person or cargo(potentially due to risk of vehicle damage, risk to other passengers,substance detection—such as drugs, bomb making materials, radioactivity,etc.); opportunities for social good, such an opportunity to donate timeand transportation which could have tax savings and implications;historical issue and incident data which could help reduce risk orprevent claims (such as claims involving a particular vehicle in aparticular area at a specific time or season); crime data; and/or userpreferences.

Moreover, the present disclosure provides for creating a hub system ofsupport, maintenance, and services to keep vehicles operating andcreating revenue 24/7/365, by, for example: (i) using onboard sensors todetect and track when an autonomous delivery service vehicle needs basicmaintenance such as oil change, tire rotation, tire inflation, newwipers, light replacement, interior/exterior detailing and route to theappropriate vendor relationship in accordance with down time routingstrategy; (ii) using onboard sensors to detect and track when anautonomous delivery service vehicle needs basic repair, such asreplacement of tires and brakes, and route to the appropriate vendorrelationship in accordance with down time routing strategy; (iii) usingon board sensors to detect and track when an autonomous delivery servicevehicle needs major repair, such as repair to exhaust, transmission,suspension, engine, body panel, etc, and route to the appropriate vendorrelationship in accordance with down time routing strategy; and/or (iv)using onboard sensors to detect and track when an autonomous deliveryservice vehicle is disabled, instantly and autonomously routing a“transfer to” vehicle (enabling continuation of people and cargo). Atthe same time, also instantly routing a tow/recovery vehicle from anappropriate vendor relationship to deliver to appropriate affiliaterepair shop. The vehicle in question may be in the process of failingand not necessarily completely disabled for the transfer to andtow/recovery vehicle to be routed.

The vehicle routing system may additionally or alternatively createultra-efficient routing and validation strategies utilizing blockchain,Al, deep learning, model training, and/or adaptive learning. Thesetechnologies in unison can enable point-by-point autonomous validationfrom origin to endpoint of booking, pickup, tracking, and delivery ofpassengers, inventory, and cargo, and mail.

The vehicle routing system may additionally or alternatively providesafety and security to passengers, cargo, and data, such as by: (i)In-route rejection of person or cargo (potentially due to risk ofvehicle damage, risk to other passengers, substance detection—such asdrugs, bomb making materials, radioactivity, etc.); (ii) autonomousnotification to proper enforcement agency of subject or package andlocation, potentially assisting in the detainment of individuals orcargo; (iii) implementing an autonomous rerouting strategy in instancesof emergency, accident, high-risk area, disaster, weather, terrorism,etc.; (iv) blockchain and AI tracking system of all packages, cargo,people, and mail; (v) detection of passenger biometric and speech datato enable quick detection and intervention in emergency situations;and/or (vi) sensor tracking/check-in or check-out of packages and mail,much like an in-store RFID tag or QR/scan code. In some embodiments, ifpackages are not scheduled to exit the vehicle, the vehicle routingsystem may enable: (a) sounding an alarm; (b) activating vehicle camerasto record and/or identify subjects; (c) activating communication devicesto report the incident. The vehicle routing system may store data usinga secure cloud service affinity partner, and/or store data through thecreation of cloud storage and data security as a service model.

The management hub application (“app”) provides management and/orsupport hub platform ecosystems as a service for corporations andprivate owners, with affinity partners and relationships. The managementhub app encompasses business and ownership models including: (i) data:capture, analysis, storage, and sharing (AI, blockchain, AWS, deeplearning, data storage and security as a service); (ii) financial andinvestment services: vehicle financing & leasing products, fleetfinancing, banking, crypto/digital currency, investments; (iii) smallbusiness services: smart reports containing metrics & opportunitiesanalysis, strategy analysis and recommendation, accounting, insurance,investments, benefits; (iv) insurance, claims, roadside assistance,loaner assistance, and accident/incident recovery services; (v)detailing, maintenance, repair, and support services; (vi) legal andfraud prevention/recovery services; (vii) advertising, marketing, app,ratings and experiences services; (viii) owner and passenger services;(ix) cargo and passenger booking, tracking, and delivery validationservices; and/or (x) subscription services to encompass all of theabove, certain packages, or a la carte add-on services.

The system and methods described herein may utilize AI, deep learning,data mining, model training, and/or adaptive learning to recognize andunderstand unseen patterns and uses, inform on those patterns and uses,and automatically recommend and develop new business models. Forexample, the vehicle routing system, with affirmative consent, maycollect data on how owners leverage self-driving technology dependent ongeography or other differences, which leads to the creation of a newbusiness model elsewhere.

The systems and methods described herein may also provide fleet purchaseand management as an investment service. For example, the vehiclerouting system may enable investors to contribute to or wholly invest ina platform where one party (e.g., the vehicle routing system or a partyassociated therewith) purchases and manages an autonomous fleet forinvestors. Investors may gain passive revenue without having tophysically own or manage one or more autonomous delivery vehicles. As anincentive, investors can also pre-pay for services enabling them toreceive free ride share services up to a certain dollar amount eachmonth as well as a % of profits. For example, an investor may invest $Xand may receive a Y % return on investment plus a $ZZZ credit forride-share service fees.

The vehicle routing system may also enable data storage and security asa service, allowing consumers to subscribe to a service that enablesthem to store and protect data including: (i) digital data; (ii)personal data; (iii) insurance data; (iv) estate data; (v) digitalcommunication and email data; (vi) financial, investment, and creditdata; (vii) digital transactions; (viii) business data; and/or (ix)employee data.

Machine Learning & Other Matters

The computer-implemented methods discussed herein may includeadditional, less, or alternate actions, including those discussedelsewhere herein. The methods may be implemented via one or more localor remote processors, transceivers, servers, and/or sensors (such asprocessors, transceivers, servers, and/or sensors mounted on vehicles ormobile devices, or associated with smart infrastructure or remoteservers), and/or via computer-executable instructions stored onnon-transitory computer-readable media or medium.

Additionally, the computer systems discussed herein may includeadditional, less, or alternate functionality, including that discussedelsewhere herein. The computer systems discussed herein may include orbe implemented via computer-executable instructions stored onnon-transitory computer-readable media or medium.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, a reinforced or reinforcement learningmodule or program, or a combined learning module or program that learnsin two or more fields or areas of interest. Machine learning may involveidentifying and recognizing patterns in existing data in order tofacilitate making predictions for subsequent data. Models may be createdbased upon example inputs in order to make valid and reliablepredictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as images, object statistics and information, historical estimates,and/or actual repair costs. The machine learning programs may utilizedeep learning algorithms that may be primarily focused on patternrecognition, and may be trained after processing multiple examples. Themachine learning programs may include Bayesian Program Learning (BPL),voice recognition and synthesis, image or object recognition, opticalcharacter recognition, and/or natural language processing—eitherindividually or in combination. The machine learning programs may alsoinclude natural language processing, semantic analysis, automaticreasoning, and/or machine learning.

Supervised and unsupervised machine learning techniques may be used. Insupervised machine learning, a processing element may be provided withexample inputs and their associated outputs, and may seek to discover ageneral rule that maps inputs to outputs, so that when subsequent novelinputs are provided the processing element may, based upon thediscovered rule, accurately predict the correct output. In unsupervisedmachine learning, the processing element may be required to find its ownstructure in unlabeled example inputs. In one embodiment, machinelearning techniques may be used to extract data about infrastructuresand users associated with a building to detect events and correlationsbetween detected events to identify trends.

Based upon these analyses, the processing element may learn how toidentify characteristics, trends, and patterns in current and/orhistorical sensor data that may then be applied to generating optimalroutes and/or generating vehicle analytics.

Exemplary Float Embodiments

In one aspect, this disclosure generally relates to autonomouslyfloating from opportunity to opportunity. More specifically, the presentembodiments relate to autonomous ride share; package, mail, people, andmaterial delivery; and/or data collection and analytics.

Autonomous vehicles are being researched and developed. Unlikeconvention vehicles that have human drivers, autonomous vehicles may notbe limited by having a main operator or driver, or may not be limited bythe driver's fatigue, schedule, or availability. A few exemplaryfloating embodiments are discussed below.

Ride-Share Drivers

In one aspect, the present embodiments involve ride-share driversdelivering passengers and packages. Current ride-share drivers (such asthose who drive for Uber and Lyft) are missing an income opportunity toalso offer local and OTR package pickup and delivery services as theytransport ride-share customers.

Delivery Fleet

In another aspect, the present embodiments will allow large corporationsto shift to an autonomous delivery fleet, and later turn to privateowners. As package delivery models shift to become autonomous, largecorporations (such as product companies Amazon, Facebook Marketplace,Google, Wink, DHgate) may purchase their own autonomous fleets toprovide their own delivery models instead of using UPS, USPS, FedEx, andother package handlers. What they may find is that purchasing autonomousfleets, and insuring and maintaining them are counter intuitive torevenue models. As a result, they may turn to private consumers who owntheir own autonomous vehicles and act as third-party service providers.

As package delivery models shift to become autonomous, some companieslike Amazon might move to a multi-purpose delivery package service. Withthe present embodiments, this service would include primarily packagedelivery for the company who owns the fleet (as an example Amazon) butwill extend out beyond delivery of (Amazon) packages to packages forother vendors, as well as delivery of people and other services. Openingthe service up to other delivery types will allow these companies to runa profitable business model in areas where package delivery alone wouldnot be profitable.

Autonomous Vehicle Opportunities

In another aspect, the present embodiments involve private drivers thathave become initially unemployed due to corporate autonomous fleetsbecoming rehired if they own autonomous vehicles. For instance, as aresult of vehicles becoming autonomous, ride-sharing companies such asUber, Lyft, and others may eliminate the need for employment of 3^(rd)party drivers (consumers who use their own cars to drive for theseservices), replacing them with their own fleet of autonomoustransportation. Thus, consumers receiving income as drivers ofconventional vehicle will no longer be employed as drivers, but may haveopportunities associated with owning an autonomous vehicle.

Private Autonomous Owners

In another aspect, the present embodiments will allow private autonomousvehicle owners to become ride-share/package delivery solutions, as wellas facilitate data capture as a service fleets. As automation decreasesincome options and opportunities in metro areas, drivers who werepreviously employed and other savvy consumers may purchase their ownautonomous multi-modal vehicle (car, truck, bike, motorcycle, bus, semi,boat/ship, plane, etc.) or micro-modal fleet to join the ranks ofautonomous multi-modal transportation, package, and data point captureservice providers. As private owners they will need business,organization, maintenance, data storage and delivery, and 5G serviceswhich will support these efforts.

Multiple Points of Data

In another aspect, the present embodiments involve autonomoustransportation and will involve much more than delivering packages andpeople. For instance, the present embodiments will also involvecapturing and delivering multiple points of data. As more autonomousvehicles take the road with advanced monitoring, tracking, and awarenesssystems, they may have the ability to track multiple data points, suchas for infrastructure, vehicle performance, crime, environmental,marketplace activity, routing, weather, maintenance, metrics, and more,and provide instant alerts and reporting.

Support Network

In another aspect, the present embodiments will provide a large supportnetwork that will provide for the corporate/private autonomousrevolution. As large and small business form their autonomoustransportation networks, they will need multiple services to support,including business loans, vehicle financing, B2B insurance, fleetinsurance, maintenance, data storage and delivery, financial, banking,investment services, subscriptions and subscription services, smartcontracts, package tracking, data management, metrics management,account management and customer services, and data security among otherneeds.

Mail Services

In another aspect, the present embodiments will provide a large supportnetwork that will provide for the corporate/private autonomousrevolution of mail services. Currently, U.S. mail is delivered using afederal system. In the future, mail delivery services may be privatizedby companies such as Amazon who depend on an overwhelmed and potentiallyoutdated delivery system. In order to speed delivery and create newmodels of revenue and cost reduction, private companies may enter themail/parcel delivery industry.

A. First Solution Element

The present embodiments include a first solution element that willcapture market, consumer, and vehicle data, automatically synthesize,and create ultra-efficient activity, routing, and logistics strategies;and provide autonomous “Float” strategy and strategy recommendations.

The present embodiments will (1) utilize AI, Cloud data storage, anddeep learning to understand real-time local, regional, and domesticmarket and consumer use patterns for ride share, package pickup anddelivery, and other cargo pickup and delivery; (2) utilize AI and deeplearning to understand and automatically employ strategies for bestclass or closest vehicle to implement, best location (local, regional,domestic, or all), maximum business and revenue opportunity, bestcompetition space or location underserved space; (3) utilize AI and deeplearning to gather information from smart things and calendars, usercalendar and social media if enabled, publicly available social media,and news sources to gain understanding of real-time events (e.g.,concerts, festivals, sporting events, rallies, etc.) and areaactivities, and employ strategies to service those areas; (4) utilize AIand deep learning to understand patterns of businesses such as Amazonthat have a large quantity of delivery/return package traffic, andlocations delivered from/returned to, to enable service to thoseproviders; (5) utilize AI and deep learning to understand and employstrategies for when a ride share, package/cargo/mail pickup/delivery, ora combination of people and cargo is appropriate for max revenuepotential; (6) utilize AI and deep learning to understand and employstrategies for when maximum safety and security is needed, such as thetransport of a child, senior, disabled, or a high-value package; (7)utilize AI and deep learning patterns to employ strategies for best downtimes for fueling, maintenance, and repair (which could be while enroute to picking up cargo or people, or delivering cargo); (8) enable anapp to show one or more vehicles' current strategy, performance,location, current use, revenue generated, maintenance/repair, vehicleissues, and AI generated recommendations for future use if not using anautonomous Float strategy; and/or (9) enable owners, subscribers, and/orinvestors to receive smart reports, and be able to adjust use andmaintenance strategies as desired.

B. Second Solution Element

The present embodiments include a second solution element that utilizeon-board cameras, sensors, and AI to capture vehicle data,infrastructure, environmental elements, road way, business, andneighborhood data points.

The present embodiments will be configured to (1) understand overall,live, real-time risks to be able to assess insurance, protection, andsolution needs; (2) utilize data to understand real-time risks andliabilities to vehicle(s), people, interstate, street, roadwaytechnology and roadway lights, buildings, houses, and landscape; (3)create automatic reports for local governance highlightinginfrastructure issues, changes, technology and construction progress andupgrade, work needing to be performed or maintained, and crime and offersolutions, strategies, and services which reduce risks, liabilities, andcosts and potentially reducing insurance claims; (4) create automaticreports highlighting business and residential consumer risks andliabilities, and offering solutions and services to reduce risks,potentially lower insurance costs, and raise property value or revenuepotential; (5) create automatic reports regarding real-time ambientenvironment including traffic, weather, disaster, and other elementswhich may elevate risk owner/user risk or claims activity; (6) capturevehicle metric data points for reporting vehicle issues and performanceback to manufacturer; (7) use these data points to create Pattern andRisk Profiles for each vehicle and its use, each area traveled in orthrough, each passenger, and potentially each vehicle owner; and/or (8)enable the passive listening and passive data capture of video, audio,and GPS, and associated reporting to proper enforcement. This could helpincrease success of responding to emergencies, accidents, disasters orfinding missing persons (amber alerts), persons of interest (wantedcriminals), crimes in progress, fires, cries for help, and provide dataevidence within an area of travel if a crime or incident has beencommitted. The Pattern and Risk Profiles could be used to createinsurance models—such as the creation of an on-demand insurance (or UBI(usage-based insurance) product or service based upon passenger and orpackages in the vehicle—which are customized to each vehicle, passenger,and cargo provider.

C. Third Solution Element

The present embodiments include a third solution element that creates acontinuous, adaptive system for right sizing the right cargo for theright vehicle, and the right complementary route at the right time. Thepresent embodiments utilize vehicle sensors and AI to create in-vehiclecargo storage/people/mail placement strategies enabling realization ofmaximum cargo load, optimum pickup/offload speed, and highest dailyrevenue.

The present embodiment utilize AI, model training, and adaptive learningto create a pickup/delivery routing strategy based upon the followingfactors: time; vehicle model and type; current vehicle location; currentcargo; dimensions of individual package/people; weight of individualpackage/people elements and total cargo; purpose of transportation;cargo or people to pickup/deliver; current location and futuredestination of person/package/cargo: start point, end point, waypoints,transfer point(s); weather; forecast; current area construction and roadclosures; active emergency route status; time requested for delivery ifpackage/cargo; passenger subscription status (future packages tiers mayenable choice of rate class, such as private direct routing or routingwith package delivery on route to destination); delivery ofperson/people, if enabled due to route direction agreement, being firstpriority; human emergency status; environmental emergency status; areaand route risk data; area events; rider or cargo provider rating; legaldata, such as protective orders limiting distance; route continuation(enablement of rider or cargo to change vehicles to be delivered atfastest pace); routing to complete route continuation strategy; in-routerejection of person or cargo (potentially due to risk of vehicle damage,risk to other passengers, substance detection—such as drugs, bomb makingmaterials, radioactivity, etc.); opportunities for social good, such asopportunity to donate time and transportation which could have taxsavings and implications; historical issue and incident data which couldhelp reduce risk or prevent claims (such as claims involving aparticular vehicle in a particular area at a specific time or season);crime data; and/or user preferences.

D. Fourth Solution Element

The present embodiments include a fourth solution element that creates ahub system of support, maintenance, and services to keep vehiclesoperating and creating revenue 24 hours a day, 7 days a week, 365 days ayear.

The present embodiments (1) use onboard sensors to detect and track whenan autonomous delivery service vehicle needs basic maintenance—such asan oil change, tire rotation, tire inflation, new wipers, lightreplacement, interior/exterior detailing—and route to the appropriatevendor relationship in accordance with down time routing strategy; (2)use onboard sensors to detect and track when an autonomous deliveryservice vehicle needs basic repair, such as replacement of tires andbrakes, and route to the appropriate vendor relationship in accordancewith down time routing strategy; and/or (3) use onboard sensors todetect and track when an autonomous delivery service vehicle needs majorrepair, such as repair to exhaust, transmission, suspension, engine,body panel, etc, and route to the appropriate vendor relationship inaccordance with down time routing strategy.

The present embodiments may also (4) use onboard sensors to detect andtrack when an autonomous delivery service vehicle is disabled, and theninstantly and autonomously route a “transfer to” vehicle (enablingcontinuation of people and cargo). At the same time, also instantlyrouting a tow/recovery vehicle from an appropriate vendor relationshipto deliver to appropriate affiliate repair shop. The vehicle in questionmay be in the process of failing, and not necessarily completelydisabled for the transfer to and tow/recovery vehicle to be routed.

E. Fifth Solution Element

The present embodiments include a fifth solution element that createsultra-efficient routing and validation strategies utilizing Blockchain,AI, Deep Learning, Model Training, and Adaptive Learning. Thesetechnologies in unison can enable point-by-point autonomous validationfrom origin to endpoint of booking, pickup, tracking, and delivery ofpassengers, inventory, and cargo, and mail.

F. Sixth Solution Element

The present embodiments include a sixth solution element that providessafety and security to passengers, cargo, and data.

The present embodiments may include (1) in-route rejection of person orcargo (potentially due to risk of vehicle damage, risk to otherpassengers, substance detection—such as drugs, bomb making materials,radioactivity, etc); (2) autonomous notification to proper enforcementagency of subject or package and location, potentially assisting in thedetainment of individuals or cargo; (3) implementing an autonomousrerouting strategy in instances of emergency, accident, high-risk area,disaster, weather, terrorism, etc.; (4) Blockchain and AI trackingsystem of all packages, cargo, people, and mail; and/or (5) sensortracking/check-in or check-out of packages and mail, much like anin-store RFID tag or QR/Scan code. If packages are not scheduled to exitthe vehicle: (a) an alarm could sound; (b) vehicle cameras couldidentify subject and record; and/or (c) communication devices couldreport the incident.

The present embodiment may also include (6) data stored using a securecloud service affinity partner; (7) data stored through the creation ofa cloud storage and data security mechanism as a service model; and/or(8) detection of passenger biometric and speech data to enable quickdetection and intervention in emergency situations.

G. Seventh Solution Element

The present embodiments include a seventh solution element that createsa separate management and support hub platform ecosystems as a service.The services may be for corporations and private owners, and may includeimplementing multiple platform ecosystems with affinity partners andrelationships.

The present embodiments may include encompassing business and ownershipmodels including: (1) Data: capture, analysis, storage, and sharing (AI,Blockchain, AWS, Deep Learning, Data Storage and Security as a Service);(2) Financial and Investment Services: Vehicle Financing & LeasingProducts, Fleet Financing, Banking, Crypto/Digital Currency,Investments; (3) Small Business Services: Smart Reports containingMetrics & Opportunities Analysis, Strategy Analysis and Recommendations,Accounting, Insurance, Investments, Benefits; (4) Insurance, Claims,Roadside Assistance, Loaner Assistance, and Accident/Incident RecoveryServices; (5) Detailing, Maintenance, Repair, and Support Services; (6)Legal and Fraud Prevention/Recovery Services; (7) Advertising,Marketing, App, Ratings and Experiences Services; (8) Owner andPassenger Services; (9) Cargo and Passenger Booking, Tracking, andDelivery Validation Services; and/or (10) Subscription Services toencompass all of the above, certain packages, or ala carte add onservices.

H. Eighth Solution Element

The present embodiments include an eighth solution element that utilizesAI, Deep Learning, Data Mining, Model Training, and Adaptive Learning torecognize and understand unseen patterns and uses, inform on thosepatterns and uses, and automatically recommend and develop new businessmodels. For example, an entity may collect data on how owners leverageself-driving technology dependent on geography or other differenceswhich leads to the creation of a new business model elsewhere.

L. Ninth Solution Element

The present embodiments include a ninth solution element that includesFleet Purchase and Management as an Investment Service. The presentembodiments enable investors to contribute to, or wholly invest in, aplatform where an entity purchases and manages an autonomous fleet forinvestors. Investors gain passive revenue without having to physicallyown or manage one or more autonomous delivery vehicles. As an incentive,investors can also pre-pay for services enabling them to receive freeride share services up to a certain dollar amount each month, as well asa % of profits. As an example, an investor invests $X and receives a X %return on investment plus a $XXX credit for ride-share service fees.

J. Tenth Solution Element

The present embodiments include a tenth solution element that providesData Storage and Security as a Service. The present embodiments enableconsumers to subscribe to a service which enables them to store andprotect data including: Digital Data; Personal Data; Insurance Data;Estate Data; Digital Communication and Email Data; Financial,Investment, and Credit Data; Digital Transactions; Business Data; and/orEmployee Data.

Exemplary Float Computing Environment

FIG. 12A depicts an exemplary FLOAT computing environment thatfacilitates autonomous vehicle autonomously floating from oneopportunity to another 1200. A remote server 1202 may collect real-timeor near real-time data from various remotely located computing devices.The remote server 1202 may be in wireless communication with the variouscomputing devices.

The remote server 1202 may wireless receive data transmission fromautonomous vehicles (autonomous passenger cars, autonomous flying cars,autonomous delivery vans, autonomous semi-trucks, etc.) 1204 and smartvehicles, smart infrastructure and smart signs 1206, satellites 1208,airplanes 1210, drones 1212, mobile devices (such as laptops, cell orsmart phones, wearables, smart watches, etc.) 1214, smart or intelligenthomes 1216, and/or smart buildings 1218.

For instance, the autonomous vehicles 1204 may have numerousvehicle-mounted sensors and processors that generate or collect data,and may include transceivers to transmit that data to the remote server1202. Likewise, the smart homes 1216 and smart buildings 1218 mayinclude home or building-mounted sensors and processors that generate orcollect data, and may include transceivers to transmit that data to theremote server 1202. Similarly, the smart drones 1212 and smart mobiledevices may have sensors and processors that generate sensor, image, orother data and have transceivers to transmit the data to the remoteserver 1202.

Exemplary Float Computer-Implemented Method

FIG. 12B depicts an exemplary FLOAT computer-implemented methodfacilitates autonomous vehicle autonomously floating from oneopportunity to another 1250. The method 1250 may be implemented via oneor more local or remote processors, sensors, transceivers, and/orservers.

The computer-implemented method 1250 may include receiving, via wirelesscommunication or data transmission, autonomous vehicle (AV) conditiondata from multiple autonomous vehicles and/or other sources at one ormore processors and/or remote servers 1252. The AV condition data may bereal-time or near real-time data. The AV condition data may be generatedor collected by one or more autonomous vehicle-mounted sensors and/orprocessors. The AV condition data may be transmitted to a remote serverfor analysis via wireless communication and/or data transmission overone or more radio frequency links.

The autonomous vehicles generating, collecting, and/or transmitting theAV condition may include automobiles, trucks, drones, planes, or othersources, including those listed in FIG. 12A. The condition data may alsobe generated, collected, and/or transmitted by other sources, such assmart infrastructure, smart buildings, and/or intelligent homes.

The method 1250 may include retrieving and/or receiving via wirelesscommunication and/or data transmission, via one or more processorsand/or remote servers, multiple electronic service pick-up and deliveryrequests within a geographical area 1254, such as a city, county, town,state, region, etc. The electronic service requests may be generatedand/or received by one or more processors associated with customermobile devices or other computing devices. The electronic servicerequests may include pick-up and drop-off point coordinates for use aswaypoints along an overall route for an autonomous delivery vehicle. Thepick-up and drop-off points coordinates may be GPS coordinates or streetaddresses, for example.

The electronic service requests may include street addresses, GPScoordinates, and/or other location data. The electronic service requestsmay include passenger, item, material, or package information, such astype, weight, hazardous material or not, flammable material or not, etc.for items or packages. The electronic service requests may includepassenger name, medical conditions, passenger preferences, age, etc. forpassengers.

The method 1250 may include calculating, via one or more processors, aroute from an origination point to a final destination that includeseach pick-up and drop-off point as a lowest cost waypoint along theroute 1256. Each lowest cost waypoint along the route may be calculatedbased upon the AV condition data received, and/or other data. Eachwaypoint may be calculated as being the lowest-cost with respect to time(such as shortest time in transit, quickest delivery time, etc.),mileage (such as shortest distance in transit), traffic (least amount oftraffic), risk (the safest intermediate route between pick-up anddrop-off points), safest weather conditions, and/or gas or electricityusage. The lowest-cost waypoints may also be calculated based uponoptimizing downtime that may be used for maintenance of the delivery AV,or the refueling or recharging of the delivery AV.

The method 1250 may include commencing routing the delivery AV along theroute calculated to pick-up and drop-off passengers and packages at thedesignated waypoints 1258. The delivery AV may autonomously travel toeach waypoint in succession along the route, unless or until an updatedoverall route to the final destination is dynamically calculated basedupon updated AV condition data received and/or new service requestsassociated with additional passengers or packages to be delivered.

The method 1250 may include receiving, via wireless communication ordata transmission over one or more radio frequency links, a new oradditional electronic service request for passenger or package pick-upand delivery 1260. The new service request may be generated and receivedfrom a customer's mobile device or other computing device. The newservice request may include information such as pick-up and drop-offlocation (such as street address or GPS coordinates), weight, name ofpassenger, type of item, type of material, etc.

The method 1250 may include, at one or more processors and/or servers,continuing to receive updated AV and infrastructure condition data, suchas updated traffic, congestion, road, and weather condition data 1262.Condition data may also be collected by mobile devices, intelligenthomes or buildings, planes, drones, etc. having sensors, processors,and/or transceivers capable of generating and transmitting conditiondata via wireless communication and/or data transmission over one ormore radio frequency links.

The method 1250 may include, while the delivery AV is floating along theroute, dynamically updating the route by calculating the lowest costroute that includes the intermediate pick-up and drop-off points withinthe additional service requests received as additional waypoints alongthe overall route to the final destination 1264. The dynamically updatedroute, in addition to accounting for, and adding, one or more additionalservice request's intermediate pick-up/drop-off points as waypoints, mayalso be updated to account for the new AV, infrastructure, and/or othercomputing device condition data received.

The method 1250 may include, via one or more processors and/or servers,continuing to route the delivery AV along the dynamically updated route1266. As the delivery AV travels its route, a passenger and status hub(or other) platform) may be updated in real-time or near real-time toshow the location and delivery status of each passenger and package. Forinstance, the location and/or status (such as waiting for pick-up, intransit, or delivered at 10 am) of each passenger or package may bedisplay in a user interface on a computer screen or mobile devicescreen.

The method 1250 may include continuing to receive additional servicerequests, and receive updated AV condition data. The method 1250 maycontinue to dynamically update the route that the AV is traveling basedupon the additional service requests and/or the updated AV conditiondata. The method 1250 may include additional, less, or alternateactions, including those discussed elsewhere herein.

In one aspect, a computer-implemented method of directing anautonomously floating delivery autonomous vehicle may be provided. Themethod may include (1) receiving, via one or more processors, servers,and/or transceivers, autonomous vehicle (AV) condition data frommultiple autonomous vehicles and other sources (such as smartinfrastructure or intelligent homes) via wireless communication and/ordata transmission over one or more radio frequency links, the conditiondata being generated by autonomous vehicle-mounted sensors andindicating weather, road, traffic, congestion, or accident conditions;(2) retrieving from a memory unit or receiving via wirelesscommunication or data transmission over one or more radio links, via oneor more processors, servers, and/or transceivers, multiple servicerequests generated by multiple customer computing devices, each servicerequest including a pick-up and drop-off address, location, orcoordinates, each service request including information identifying oneor more passengers or type and weight of one or more packages; (3)calculating, via one or more processors or servers, an overall routethat the floating delivery autonomous vehicle will travel from anorigination point to a final destination, the overall route includingeach pick-up and drop-off point of the passengers and packages (asidentified in the service requests) as a lowest cost waypoint along orwithin the overall route, the waypoints being calculated as being lowestcost based upon AV condition data and/or passenger or package weight orsize information; (4) routing or directing, via one or more processorsor servers, the delivery autonomous vehicle along the overall route topick-up and drop-off passengers and packages at each intermediatewaypoint along the overall route; (5) receiving, via one or moreprocessors or servers, a new or additional electronic service requestfor pick-up and delivery of an additional passenger and/or package, theadditional electronic service request including pick-up and drop-offpoint or location information; (6) continuing, via one or moreprocessors, servers, and/or transceivers, to receive updated AVcondition data via wireless communication or data transmission over oneor more radio links, the condition data including traffic, weather,congestion, road, and/or accident data, the condition data may begenerated by autonomous vehicles or other sources, such as smartinfrastructure, intelligent homes, or mobile devices; (7) while thedelivery AV is floating along the overall route, dynamically updatingthe overall route, via one or more processors and/or servers, bycalculating the lowest cost route that includes the intermediate pick-upand drop-off locations or points in the additional service request(s) asadditional waypoints along the overall route, the lowest cost updateroute may be calculated based upon the update AV condition data or otherdata received since the overall route was initially calculated; (8)directing, via one or more processors and/or servers, the delivery AValong the dynamically updated overall route; and/or (9) updating, viaone or more processors and/or servers, a status hub or user interfacedisplaying a current status and/or location of each passenger and/orpackage. The method may include additional, less, or alternate actions,including those discussed elsewhere herein.

For instance, the AV condition data may be generated or collected byautonomous vehicle-mounted sensors, and includes telematics (includingspeed, GPS location, route, direction, cornering, braking, andacceleration data) and image data. Additionally or alternatively, the AVcondition data may be generated or collected by smart infrastructure,mobile devices, intelligent homes, drones, planes, and/or satellites.

The lowest cost overall route may be calculated based upon shortest timeor shortest distance traveled to complete to overall route. Additionallyor alternatively, the lowest cost overall route may be calculated basedupon optimizing downtime of the delivery autonomous vehicle formaintenance purposes. Additionally or alternatively, the lowest costoverall route may be calculated based upon optimizing downtime of thedelivery autonomous vehicle for refueling or recharging purposes.Additionally or alternatively, the lowest cost overall route may becalculated based upon optimizing safety of traveling the overall route,and/or based upon weather along the route, and dropping and picking uppackages or passengers in clear or sunny weather. Additionally oralternatively, the lowest cost overall route may be calculated basedupon the least traffic, congestion, or road construction along theroute.

In some embodiments, the sensor and other data may be transmitted viawireless communication or data transmission over one or more radiofrequency links. In other embodiments, the sensor and other data may betransferred via other more advanced or unknown forms of datatransference methods and techniques.

In another aspect, a computer system for remotely and/or locallydirecting an autonomously floating delivery autonomous vehicle may beprovided. The computer system may include one or more local or remoteprocessors, sensors, transceivers, and/or servers configured to: (1)receive autonomous vehicle (AV) condition data from multiple autonomousvehicles and other sources (such as smart infrastructure or intelligenthomes) via wireless communication and/or data transmission over one ormore radio frequency links, the condition data being generated byautonomous vehicle-mounted sensors and indicating weather, road,traffic, congestion, or accident conditions; (2) retrieve from a memoryunit or receive via wireless communication or data transmission over oneor more radio links multiple service requests generated by multiplecustomer computing devices, each service request including a pick-up anddrop-off address, location, or coordinates, each service requestincluding information identifying one or more passengers or type andweight of one or more packages; (3) calculate an overall route that thefloating delivery autonomous vehicle will travel from an originationpoint to a final destination, the overall route including each pick-upand drop-off point of the passengers and packages (as identified in theservice requests) as a lowest cost waypoint along or within the overallroute, the waypoints being calculated as being lowest cost based upon AVcondition data and/or passenger or package weight or size information;(4) route or direct the delivery autonomous vehicle along the overallroute to pick-up and drop-off passengers and packages at eachintermediate waypoint along the overall route; (5) receive a new oradditional electronic service request for pick-up and delivery of anadditional passenger and/or package, the additional electronic servicerequest including pick-up and drop-off point or location information;(6) continue to receive updated AV condition data via wirelesscommunication or data transmission over one or more radio links, thecondition data including traffic, weather, congestion, road, and/oraccident data, the condition data may be generated by autonomousvehicles or other sources, such as smart infrastructure, intelligenthomes, or mobile devices; (7) while the delivery AV is floating alongthe overall route, dynamically update the overall route by calculatingthe lowest cost route that includes the intermediate pick-up anddrop-off locations or points in the additional service request(s) asadditional waypoints along the overall route, the lowest cost updateroute may be calculated based upon the update AV condition data or otherdata received since the overall route was initially calculated; (8)direct the delivery AV along the dynamically updated overall route;and/or (9) update a status hub or user interface displaying a currentstatus and/or location of each passenger and/or package. The AVcondition data may be generated or collected by autonomousvehicle-mounted sensors, and includes telematics (including speed, GPSlocation, route, direction, cornering, braking, and acceleration data)and image data, and/or generated or collected by smart infrastructure,mobile devices, intelligent homes, drones, planes, and/or satellites.The computer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

Additional Considerations

As will be appreciated based upon the foregoing specification, theabove-described embodiments of the disclosure may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware or any combination or subset thereof. Anysuch resulting program, having computer-readable code means, may beembodied or provided within one or more computer-readable media, therebymaking a computer program product, i.e., an article of manufacture,according to the discussed embodiments of the disclosure. Thecomputer-readable media may be, for example, but is not limited to, afixed (hard) drive, diskette, optical disk, magnetic tape, semiconductormemory such as read-only memory (ROM), SD card, memory device and/or anytransmitting/receiving medium, such as the Internet or othercommunication network or link. The article of manufacture containing thecomputer code may be made and/or used by executing the code directlyfrom one medium, by copying the code from one medium to another medium,or by transmitting the code over a network.

These computer programs (also known as programs, software, softwareapplications, “apps”, or code) include machine instructions for aprogrammable processor, and can be implemented in a high-levelprocedural and/or object-oriented programming language, and/or inassembly/machine language. As used herein, the terms “machine-readablemedium” and “computer-readable medium” refer to any computer programproduct, apparatus and/or device (e.g., magnetic discs, optical disks,memory, Programmable Logic Devices (PLDs)) used to provide machineinstructions and/or data to a programmable processor, including amachine-readable medium that receives machine instructions as amachine-readable signal. The “machine-readable medium” and“computer-readable medium,” however, do not include transitory signals.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

As used herein, a processor may include any programmable systemincluding systems using micro-controllers, reduced instruction setcircuits (RISC), application specific integrated circuits (ASICs), logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are example only, and arethus not intended to limit in any way the definition and/or meaning ofthe term “processor.”

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by aprocessor, including RAM memory, ROM memory, EPROM memory, EEPROMmemory, and non-volatile RAM (NVRAM) memory. The above memory types areexample only, and are thus not limiting as to the types of memory usablefor storage of a computer program.

In one embodiment, a computer program is provided, and the program isembodied on a computer-readable medium. In one exemplary embodiment, thesystem is executed on a single computer system, without requiring aconnection to a server computer. In a further exemplary embodiment, thesystem is being run in a Windows® environment (Windows is a registeredtrademark of Microsoft Corporation, Redmond, Wash.). In yet anotherembodiment, the system is run on a mainframe environment and a UNIX®server environment (UNIX is a registered trademark of X/Open CompanyLimited located in Reading, Berkshire, United Kingdom). In a furtherembodiment, the system is run on an iOS® environment (iOS is aregistered trademark of Cisco Systems, Inc. located in San Jose,Calif.). In yet a further embodiment, the system is run on a Mac OS®environment (Mac OS is a registered trademark of Apple Inc. located inCupertino, Calif.). In still yet a further embodiment, the system is runon Android® OS (Android is a registered trademark of Google, Inc. ofMountain View, Calif.). In another embodiment, the system is run onLinux® OS (Linux is a registered trademark of Linus Torvalds of Boston,Mass.). The application is flexible and designed to run in variousdifferent environments without compromising any major functionality. Thefollowing detailed description illustrates embodiments of the disclosureby way of example and not by way of limitation. It is contemplated thatthe disclosure has general application to providing an on-demandecosystem in industrial, commercial, and residential applications.

In some embodiments, the system includes multiple components distributedamong a plurality of computing devices. One or more components may be inthe form of computer-executable instructions embodied in acomputer-readable medium. The systems and processes are not limited tothe specific embodiments described herein. In addition, components ofeach system and each process can be practiced independent and separatefrom other components and processes described herein. Each component andprocess can also be used in combination with other assembly packages andprocesses. The present embodiments may enhance the functionality andfunctioning of computers and/or computer systems.

As used herein, an element or step recited in the singular and precededby the word “a” or “an” should be understood as not excluding pluralelements or steps, unless such exclusion is explicitly recited.Furthermore, references to “example embodiment” or “one embodiment” ofthe present disclosure are not intended to be interpreted as excludingthe existence of additional embodiments that also incorporate therecited features.

The patent claims at the end of this document are not intended to beconstrued under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being expressly recited in the claim(s).

This written description uses examples to disclose the disclosure,including the best mode, and also to enable any person skilled in theart to practice the disclosure, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the disclosure is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal language of the claims.

We claim:
 1. A vehicle routing and analytics (VRA) computing device forgenerating an optimal route for a vehicle to travel that maximizespotential revenue for operation of the vehicle and generating analyticsassociated with operation of the vehicle, the VRA computing devicecommunicatively coupled to the vehicle, the vehicle having a pluralityof sensors disposed thereon and configured to collect sensor data duringoperation thereof, wherein the VRA computing device comprises at leastone processor in communication with a memory, wherein the at least oneprocessor is programmed to: receive, from a vehicle user associated withthe vehicle, a vehicle definition for the vehicle, the vehicledefinition including availability parameters and delivery preferencesassociated with the vehicle; generate, based in part on the vehicledefinition, an optimal route for the vehicle that includes a scheduledlist of a plurality of tasks for the vehicle to perform, each taskhaving an associated respective cargo to be delivered, pick-up time,delivery time, pick-up location, delivery location, and task value forcompletion of the respective task, wherein the optimal route maximizesthe potential revenue for operation of the vehicle and completion of theplurality of tasks within a period of time associated with the optimalroute; transmit the optimal route to the vehicle for operation of thevehicle according to the optimal route; receive, from the vehicle,sensor data during operation of the vehicle according to the optimalroute, wherein the sensor data is generated at sensors disposed in atleast one of: (i) the vehicle, and (ii) a user computing device of thevehicle user; process the received sensor data to generate vehicleanalytics associated with a performance of the vehicle, the vehicleanalytics including a level of adherence to the optimal route andrevenue statistics for a completed portion of the optimal route and foran uncompleted portion of the optimal route; generate one or more visualrepresentations of the vehicle analytics and instructions for display ofthe one or more visual representations of the vehicle analytics; andtransmit the instructions to the user computing device of the vehicleuser, the instructions causing execution of a management hub applicationon the user computing device to display the one or more visualrepresentations with a user interface of the executed management hubapplication.
 2. The VRA computing device of claim 1, wherein the one ormore visual representations include one or more graphs, charts, maps,plots, and legends.
 3. The VRA computing device of claim 1, wherein thevehicle analytics further include one or more of: percentage of theoptimal route completed, path and distance travelled during operation ofthe vehicle according to the optimal route, current location of thevehicle, service status, services performed on the vehicle, risk ratingof the optimal route, costs of operating the vehicle during the optimalroute, revenue statistics based upon divisions of time, or revenuestatistics per task.
 4. The VRA computing device of claim 1, wherein thevehicle includes a first vehicle, the optimal route includes a firstoptimal route, and the vehicle analytics include first vehicleanalytics, wherein the VRA computing device is communicatively coupledto a plurality of vehicles including the first vehicle, each vehicle ofthe plurality of vehicles having a plurality of sensors disposed thereonand configured to collect sensor data during operation of the respectivevehicle, wherein the at least one processor is further programmed to:generate second vehicle analytics for at least one other vehicle thanthe first vehicle based upon sensor data received from the at least oneother vehicle during operation of the at least one other vehicleaccording to a respective other optimal route; compare the first vehicleanalytics to the second vehicle analytics; identify, based upon thecomparing, a difference between the first vehicle analytics and thesecond vehicle analytics; parse the vehicle definition for the firstvehicle to identify a user-defined delivery preference that accounts forthe difference between the first vehicle analytics and the secondvehicle analytics; generate a recommendation that the vehicle usermodify the user-defined delivery preference; and transmit therecommendation to the user computing device for display within the userinterface of the management hub application, the recommendationidentifying the difference between the first vehicle analytics and thesecond vehicle analytics, the user-defined delivery preference, and analternative user-defined delivery preference that would reduce thedifference between the first vehicle analytics and the second vehicleanalytics.
 5. The VRA computing device of claim 4, wherein theuser-defined delivery preference is one of a person-only cargo deliverypreference and an object-only cargo delivery preference, and wherein thealternative user-defined delivery preference is a persons-and-objectsdelivery preference.
 6. The VRA computing device of claim 1, wherein theVRA computing device is further communicatively coupled to a pluralityof service vendor computing devices associated with a respectiveplurality of service vendors that perform vehicle maintenance and repairservices, and wherein the at least one processor is further programmedto: detect, based upon the vehicle analytics, the vehicle needs aservice to be performed thereon; transmit, to the vehicle, a serviceschedule including the service to be performed, a service timeassociated with the service, and a first service vendor of the pluralityof service vendors to perform the service; transmit, to the servicevendor computing device associated with the first service vendor, theservice schedule; and transmit, to the user computing device, an alertthat the vehicle needs the service and an identification of the firstservice vendor.
 7. The VRA computing device of claim 1, wherein the atleast one processor is further programmed to: identify a plurality ofother vehicles associated with the vehicle user; generate respectivevehicle analytics for each of the plurality of other vehicles; andtransmit one or more visual representations of the vehicle analytics forthe plurality of other vehicles to the user computing device for displaywithin the user interface of the management hub application.
 8. The VRAcomputing device of claim 1, wherein the vehicle includes a firstvehicle, the optimal route includes a first optimal route, and thevehicle analytics include first vehicle analytics, wherein the VRAcomputing device is communicatively coupled to a plurality of vehiclesincluding the first vehicle, each vehicle of the plurality of vehicleshaving a plurality of sensors disposed thereon and configured to collectsensor data during operation of the respective vehicle, wherein the atleast one processor is further programmed to: generate second vehicleanalytics for the plurality of vehicles based upon sensor data receivedfrom the plurality of vehicles during operation of the plurality ofvehicles; and identify, using artificial intelligence and deep learningfunctionality, one or more patterns or trends of operation of theplurality of vehicles in the second vehicle analytics.
 9. The VRAcomputing device of claim 8, wherein the at least one processor isfurther programmed to: identify, from the one or more identifiedpatterns or trends, at least one underserved location that hasexperienced less than a threshold amount of vehicle operation therein;determine, based upon the vehicle definition of the first vehicle, thatthe availability parameters of the first vehicle do not encompass the atleast one underserved location; generate a recommendation that thevehicle user modify the availability parameters of the first vehicle toinclude the at least one underserved location, the recommendationidentifying the at least one underserved location and the availabilityparameters of the first vehicle; and transmit the recommendation to theuser computing device for display within the user interface of themanagement hub application.
 10. A computer-implemented method forgenerating an optimal route for a vehicle to travel that maximizespotential revenue for operation of the vehicle and generating analyticsassociated with operation of the vehicle, the method implemented by avehicle routing and analytics (VRA) computing device including at leastone processor and communicatively coupled to the vehicle, the vehiclehaving a plurality of sensors disposed thereon and configured to collectsensor data during operation thereof, wherein the method comprises:receiving, from a vehicle user associated with the vehicle, a vehicledefinition for the vehicle, the vehicle definition includingavailability parameters and delivery preferences associated with thevehicle; generating, based in part on the vehicle definition, an optimalroute for the vehicle that includes a scheduled list of a plurality oftasks for the vehicle to perform, each task having an associatedrespective cargo to be delivered, pick-up time, delivery time, pick-uplocation, delivery location, and task value for completion of therespective task, wherein the optimal route maximizes the potentialrevenue for operation of the vehicle and completion of the plurality oftasks within a period of time associated with the optimal route;transmitting the optimal route to the vehicle for operation of thevehicle according to the optimal route; receiving, from the vehicle,sensor data during operation of the vehicle according to the optimalroute, wherein the sensor data is generated at sensors disposed in atleast one of: (i) the vehicle, and (ii) a user computing device of thevehicle user; processing the received sensor data to generate vehicleanalytics associated with a performance of the vehicle, the vehicleanalytics including a level of adherence to the optimal route andrevenue statistics for a completed portion of the optimal route and foran uncompleted portion of the optimal route; generating one or morevisual representations of the vehicle analytics and instructions fordisplay of the one or more visual representations of the vehicleanalytics; and transmitting the instructions to the user computingdevice of the vehicle user, the instructions causing execution of amanagement hub application on the user computing device to display theone or more visual representations within a user interface of theexecuted management hub application.
 11. The computer-implemented methodof claim 10, wherein the one or more visual representations include oneor more graphs, charts, maps, plots, and legends.
 12. Thecomputer-implemented method of claim 10, wherein the vehicle analyticsfurther include one or more of: percentage of the optimal routecompleted, path and distance travelled during operation of the vehicleaccording to the optimal route, current location of the vehicle, servicestatus, services performed on the vehicle, risk rating of the optimalroute, costs of operating the vehicle during the optimal route, revenuestatistics based upon divisions of time, or revenue statistics per task.13. The computer-implemented method of claim 10, wherein the vehicleincludes a first vehicle, the optimal route includes a first optimalroute, and the vehicle analytics include first vehicle analytics,wherein the VRA computing device is communicatively coupled to aplurality of vehicles including the first vehicle, each vehicle of theplurality of vehicles having a plurality of sensors disposed thereon andconfigured to collect sensor data during operation of the respectivevehicle, wherein the method further comprises: generating second vehicleanalytics for at least one other vehicle than the first vehicle basedupon sensor data received from the at least one other vehicle duringoperation of the at least one other vehicle according to a respectiveother optimal route; comparing the first vehicle analytics to the secondvehicle analytics; identifying, based upon the comparing, a differencebetween the first vehicle analytics and the second vehicle analytics;parsing the vehicle definition for the first vehicle to identify auser-defined delivery preference that accounts for the differencebetween the first vehicle analytics and the second vehicle analytics;generating a recommendation that the vehicle user modify theuser-defined delivery preference; and transmitting the recommendation tothe user computing device for display within the user interface of themanagement hub application, the recommendation identifying thedifference between the first vehicle analytics and the second vehicleanalytics, the user-defined delivery preference, and an alternativeuser-defined delivery preference that would reduce the differencebetween the first vehicle analytics and the second vehicle analytics.14. The computer-implemented method of claim 13, wherein theuser-defined delivery preference is one of a person-only cargo deliverypreference and an object-only cargo delivery preference, and wherein thealternative user-defined delivery preference is a persons-and-objectsdelivery preference.
 15. The computer-implemented method of claim 10,wherein the VRA computing device is communicatively coupled to aplurality of service vendor computing devices associated with arespective plurality of service vendors that perform vehicle maintenanceand repair services, and wherein the method further comprises:detecting, based upon the vehicle analytics, the vehicle needs a serviceto be performed thereon; transmitting, to the vehicle, a serviceschedule including the service to be performed, a service timeassociated with the service, and a first service vendor of the pluralityof service vendors to perform the service; transmitting, to the servicevendor computing device associated with the first service vendor, theservice schedule; and transmitting, to the user computing device, analert that the vehicle needs the service and an identification of thefirst service vendor.
 16. The computer-implemented method of claim 10,wherein the method further comprises: identifying a plurality of othervehicles associated with the vehicle user; generating respective vehicleanalytics for each of the plurality of other vehicles; and transmittingone or more visual representations of the vehicle analytics for theplurality of other vehicles to the user computing device for displaywithin the user interface of the management hub application.
 17. Thecomputer-implemented method of claim 10, wherein the vehicle includes afirst vehicle, the optimal route includes a first optimal route, and thevehicle analytics include first vehicle analytics, wherein the VRAcomputing device is communicatively coupled to a plurality of vehiclesincluding the first vehicle, each vehicle of the plurality of vehicleshaving a plurality of sensors disposed thereon and configured to collectsensor data during operation of the respective vehicle, wherein themethod further comprises: generating second vehicle analytics for theplurality of vehicles based upon sensor data received from the pluralityof vehicles during operation of the plurality of vehicles; andidentifying, using artificial intelligence and deep learningfunctionality, one or more patterns or trends of operation of theplurality of vehicles in the second vehicle analytics.
 18. Thecomputer-implemented method of claim 17, wherein the method furthercomprises: identifying, from the one or more identified patterns ortrends, at least one underserved location that has experienced less thana threshold amount of vehicle operation therein; determining, based uponthe vehicle definition of the first vehicle, that the availabilityparameters of the first vehicle do not encompass the at least oneunderserved location; generating a recommendation that the vehicle usermodify the availability parameters of the first vehicle to include theat least one underserved location, the recommendation identifying the atleast one underserved location and the availability parameters of thefirst vehicle; and transmitting the recommendation to the user computingdevice for display within the user interface of the management hubapplication.
 19. At least one non-transitory computer-readable storagemedia having computer-executable instructions embodied thereon forgenerating an optimal route for a vehicle to travel that maximizespotential revenue for operation of the vehicle and generating analyticsassociated with operation of the vehicle, wherein when executed by aprocessor of a vehicle routing and analytics (VRA) computing devicecommunicatively coupled to the vehicle, the vehicle having a pluralityof sensors disposed thereon and configured to collect sensor data duringoperation thereof, the computer-executable instructions cause theprocessor to: receive, from a vehicle user associated with the vehicle,a vehicle definition for the vehicle, the vehicle definition includingavailability parameters and delivery preferences associated with thevehicle; generate, based in part on the vehicle definition, an optimalroute for the vehicle that includes a scheduled list of a plurality oftasks for the vehicle to perform, each task having an associatedrespective cargo to be delivered, pick-up time, delivery time, pick-uplocation, delivery location, and task value for completion of therespective task, wherein the optimal route maximizes the potentialrevenue for operation of the vehicle and completion of the plurality oftasks within a period of time associated with the optimal route;transmit the optimal route to the vehicle for operation of the vehicleaccording to the optimal route; receive, from the vehicle, sensor dataduring operation of the vehicle according to the optimal route, whereinthe sensor data is generated at sensors disposed in at least one of: (i)the vehicle, and (ii) a user computing device of the vehicle user;process the received sensor data to generate vehicle analyticsassociated with a performance of the vehicle, the vehicle analyticsincluding a level of adherence to the optimal route and revenuestatistics for a completed portion of the optimal route and for anuncompleted portion of the optimal route; generate one or more visualrepresentations of the vehicle analytics and instructions for display ofthe one or more visual representations of the vehicle analytics; andtransmit instructions to the user computing device of the vehicle user,the instructions causing execution of a management hub application onthe user computing device to display the one or more visualrepresentations within a user interface of the executed management hubapplication.
 20. The computer-readable storage media of claim 19,wherein the one or more visual representations include one or moregraphs, charts, maps, plots, and legends.